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Friday, October 7

Room/Location

Suzhou

Organizer(s)

Xiaobo Zhu, Adriaan Rol, Xiang Fu, and Lingling Lao, Yuxin Deng

Description

The rapid development of quantum computers has entailed extensive research in quantum controlling technologies that manipulate quantum states. Equally important to quantum controlling hardware is the development of software, which greatly improves the efficiency of quantum experiments or the applications of quantum computers in solving real world problems. In this proposal, a half-day tutorial is proposed. It contains four sessions and covers the following topics: quantum programming languages, quantum compilation, quantum controlling software, and quantum computing based on superconducting qubits. The intended audience includes but not limited to experts and students in the fields of (1) quantum computing, (2) compiler and architecture optimization, (3) hardware/software co-design. Since quantum computing is a sub-area in EDA conferences, programming language, compilation, systematic software support for controlling superconducting quantum computing systems will fit the audience from ESWEEK.

Room/Location

Hangzhou

Organizer(s)

Naijun Zhan and Mingshuai Chen

Description

The advent of systems of cooperative cyber-physical systems draws attention to a central problem of networked and distributed control systems: the ubiquity of delay in feedback loops between logically or spatially distributed components, which is not adequately re ected in traditional models of hybrid-state dynamics based on ordinary differential equations and immediate transitions. The occurrence of feedback delays may significantly alter a system’s dynamic response. Unmodelled delays in a control loop consequently have the potential to invalidate any stability or safety certificate obtained on a related delay-free model, which is the current practice in hybrid- system analysis. In this tutorial, we will present various approaches to the analysis and correct-by-construction design of dynamical systems subject to delayed information exchange, as pertinent to distributed hybrid systems. We will explain automatic verification procedures for invariance properties over bounded or unbounded temporal horizons. This analytical view will be complemented by a constructive one for synthesizing delay-resilient control strategies for discrete and hybrid discrete-continuous dynamics.

Room/Location

Zhujiaojiao

Organizer(s)

Joshua Lu and Yajun Ha

Description

AMD-Xilinx is the inventor of the FPGA, programmable SoCs, and now, the ACAP. Our highly flexible programmable silicon, enabled by a suite of advanced software and tools, drives rapid innovation across a wide span of industries and technologies – from consumer to cars to the cloud. The goal of this tutorial is to introduce the Vitis software development environment for designing accelerators for embedded systems using Vitis.. Attendees will have the opportunity to learn how to use these tools, test the tutorial examples on the target boards, and explore the latest Vitis AI, Versal AIE technologies.

Room/Location

Hangzhou

Organizer(s)

Janardhan Rao Doppa and Biresh Kumar Joardar

Description

Training machine learning (ML) models at the edge (on-chip training on end user devices) can address many pressing challenges including data privacy/security, increase the accessibility of ML applications to different parts of the world by reducing the dependence on the communication fabric and the cloud infrastructure, and meet the real-time requirements of AR/VR applications. However, existing edge platforms do not have sufficient computing capabilities to support complex ML tasks such as training large CNNs. This tutorial will consider solutions based on Resistive Random-access Memories (ReRAMs) to address these challenges and answer the following questions: (1) How to use ReRAMs as a Processing-in-memory (PIM) architecture? (2) How to map machine learning techniques to ReRAMbased manycore systems to improve performance and energy-efficiency? (3) What are the challenges associated with different types of Deep Learning applications (such as CNNs and GNNs) when mapped to PIM-based manycore architectures? (4) How can we ensure reliability in these architectures? To address these outstanding challenges, outof- the-box approaches need to be explored. By integrating machine learning algorithms, data analytics, statistical modeling, and design of advanced computing systems, this tutorial will engage a broad section of ESWEEK conference attendees. It also highlights how machine learning and embedded system researchers can join hands to design energyefficient and reliable miniaturized computing systems. This tutorial is targeted towards university researchers/professors, MS/Ph.D. students, professionals from industry, and IC designers, who want to learn how to use ReRAMs for ML applications, as well as experienced researchers looking for exciting new directions in PIM. We expect at least 30-40 attendees. We will announce the tutorial through our regular networks, as well as mailing lists of CEDA, IEEE CAS Society, IEEE Computer Society and ACM SIGDA (SIGDA E-News that reaches thousands of EDA professionals, and we will leverage it to publicize our tutorial). The target audience matches the typical ESWEEK participant very well. We also hope that this tutorial will allow ESWEEK to reach out to a wider audience and help boost attendance.

Room/Location

Zhujiaojiao

Organizer(s)

Prabhat Mishra

Description

System-on-Chip (SoC) is the brain behind computing and communication in a wide variety of systems. Reusable hardware Intellectual Property (IP) based System-on-Chip (SoC) design has emerged as a pervasive design practice in the industry to dramatically reduce design and verification cost while meeting aggressive time-to-market constraints. Growing reliance on these pre-verified hardware IPs, often gathered from untrusted third-party vendors, severely affects the security and trustworthiness of SoC computing platforms. These third-party IPs may come with deliberate malicious implants to incorporate undesired functionality (e.g., hardware Trojans), undocumented test/debug interface working as hidden backdoor, or other integrity issues. It is extremely difficult to verify integrity and trust of hardware IPs due to several reasons including (a) lack of a golden reference model or incomplete specification, (b) exponential space of diverse types of complex IPs and IP-specific vulnerabilities, (c) lack of automated and scalable CAD tools for IP trust verification, and (d) lack of security metrics to measure the security robustness of a given design or mitigation technique. While functional validation has received significant attention over the years, it is critical to perform “security and trust verification” for designing trustworthy systems.

This tutorial will provide a comprehensive overview of both fundamental concepts and recent advances in hardware security and trust validation using simulation-based approaches, formal methods as well as side-channel analysis. Specifically, the tutorial will consist of four parts. The first part will introduce security vulnerabilities (threats) and various challenges associated with trust validation of hardware IPs. It will highlight recent advances in developing trust metrics and benchmarks. The second part will cover assertion-based security validation utilizing automated generation of security assertions. It will also cover automated test generation techniques for activation of security assertions. The third part will describe how formal verification techniques (including model checking, SAT solving, theorem proving and equivalence checking) can be effectively utilized for validation of hardware security vulnerabilities. The fourth part will discuss how side-channel analysis can be effectively utilized to detect malicious implants. It will conclude with a discussion on integration of security verification in existing functional validation methodology.

Saturday, October 8

Room/Location

Suzhou

Organizer(s)

Ahmad-Reza Sadeghi

Description

The science of systems security is concerned with security aspects of computing systems at both software and hardware layers. The ever-increasing complexity of computing systems, emerging technologies such as IoT and AI, and advancing attack capabilities pose a variety of (new) challenges on the design and implementation of security concepts, methods and mechanisms.
This talk provides an overview of our journey through the systems security research universe. It points out several aspects of advancing software security and hardware-assisted security in academia and industry. A particular focus of the talk is devoted to the current crucial security threat posed by software-exploitable hardware vulnerabilities that put our critical systems, and hence our society, at risk. Finally, we discuss our future vision and new research directions in systems security.

Room/Location

Hangzhou

Organizer(s)

Partha Roop

Description

Many biomedical systems use embedded controllers to control physical processes, and consequently form a class of Cyber-Physical Systems (CPSs). Examples range from pacemakers to automated insulin pumps. These systems must work safely at all times. In this tutorial, we will focus on a design methodology for such CPSs.

We rely on the well-known synchronous approach for modelling the biological processes i.e. the human organ in question at a suitable abstraction-level as well as the medical device. Using the synchronous approach helps in both modelling and verification as well as automated code generation. The synchronous approach provides well known benefits such as deterministic execution, which is an ideal fit for ensuring safety. This tutorial will introduce a systematic design approach starting with modelling of biomedical devices using the SCCharts synchronous language. Next, the Intel NIOS II platform will be introduced as a means of prototyping a given medical device. This prototype device can then be run in closed-loop with a real-time version of the associated human organ. We will use a cardiac pacemaker as a running example and will therefore briefly introduce modelling of the cardiac conduction system using compositional models. We will then demonstrate how the pacemaker can be tested, in closed-loop, with the adjoining model of the cardiac conduction system. Additionally, we will present an approach for the formal verification of this device, using the tool UPPAAL and associated properties to ensure its correct operation. We will also present how pacemakers can be secured in the face of adversarial attacks, before presenting key areas for future research.

Room/Location

Zhujiaojiao

Organizer(s)

Nele Mentens

Description

Field-Programmable Gate Arrays (FPGAs) are configurable hardware architectures that combine the performance of Application-Specific Integrated Circuits (ASICs) with the programmability of microprocessors. FPGAs are popular implementation platforms for cryptography and security applications. FPGAs are used as accelerators for cryptographic algorithms and network security solutions, and as patchable trusted computing platforms. This lecture will first introduce the technology and design flow of FPGAs. Next, the lecture will concentrate on the use of FPGAs as cryptographic accelerators, as network security solutions and as trusted computing platforms.

Room/Location

Suzhou

Organizer(s)

Farinaz Koushanfar

Description

Deep Learning (DL) has been increasingly deployed in various real-world applications due to its unprecedented performance and automated capability of learning hidden representations. While DL can achieve high task performance, the training process of a DL model is both time and resource-consuming. Therefore, current supply chains of the DL models assume the customers obtain pre-trained Deep Neural Networks (DNNs) from the third-party providers that have sufficient computing power. In the centralized setting, the model designer trains the DL model using the local dataset. However, the collected training data may contain erroneous or poisoned data points. The model designer might craft malicious training samples and inject backdoors in the DL model distributed to the users. As a result, the user’s model will malfunction. In the federated learning setting, the cloud server aggregates local models trained on individual local datasets and updates the global model. In this scenario, the local client could poison the local training set and/or arbitrarily manipulate the local update. If the cloud server incorporates the malicious local gradients in model aggregation, the resulting global model will have degraded performance or backdoor behaviors. In this class, we present a comprehensive overview of contemporary data poisoning and model poisoning attacks against DL models in both centralized and federated learning scenarios. In addition, we review existing detection and defense techniques against various poisoning attacks.

Room/Location

Hangzhou

Organizer(s)

Christof Fetzer

Description

An application might run in a cloud, e.g., an edge cloud with limited physical security. Or, the cloud provider might be in a different jurisdiction. We introduce the fundamental concepts of confidential computing and show how one can ensure the confidentiality, integrity, and consistency of applications – even if we cannot trust the external provider. We will also show how to convert a cloud-native application into a cloud-confidential application.

Room/Location

Zhujiaojiao

Organizer(s)

Debdeep Mukhopadhyay

Description

The lecture would provide an overview on fault attacks on modern cryptosystems. We shall start with some classical fault based cryptanalysis of the Advanced Encryption Standard (AES), called Differential Fault Analysis (DFA). Later we discuss another class of fault attacks, called Differential. Fault Intensity Attacks (DFIA), and show how fault bias can be utilized to break redundancy based countermeasures based on classical fault tolerance. In the later part of the talk, we present an overview on some of the advanced fault attack techniques, namely Statistical Ineffective Fault Attacks (SIFA), and Fault Template Attacks (FTA). We also aim to discuss on suitable techniques to thwart these menacing classes of physical attacks. The tutorial would be accompanied with small demonstrations to elucidate the concepts presented.

Sunday, October 9

Room/Location

Suzhou

Organizer(s)

Abhik Roychoudhury

Description

Fuzz testing is a popular technique for detecting security vulnerabilities in software systems. It proceeds by compile time instrumentation, along with a run-time biased random search to find crashing inputs. The biased random search may be guided by an objective function or by logical constraints leading to different testing setups. In this paper, we will share various mechanisms and viewpoints in adapting or adopting greybox fuzzing for embedded software. This is of increased importance due to increased movement of the attack surface towards the edge. Moreover, as the security vulnerabilities are found and published, the software systems suffer from increased exposure, which can be alleviated by automated program repair technologies. In a synergistic setting, the searches over program edits in program repair, and the search over program inputs can strengthen each other. The tutorial give the audience wide exposure on greybox fuzzing and whitebox fuzzing (also known as symbolic execution) technologies, as well as forward looking techniques for automated program repair, which are seeing increased adoption.

Room/Location

Hangzhou

Organizer(s)

Thorsten Strufe

Description

The evolution from desktops over mobile devices to smart-X has brought near perfect visibility on the behavior and whereabouts of citizens, both in the digital and the real world. This comes with numerous features and simplifications of processes, and increasing utility for users and observed individuals. It does raise concerns about the potential for abuse, from unexpected identification to the disclosure of sensitive characteristics, health conditions, or personal peculiarities, despite constant claims of “anonymization” and “GDPR compliance”.

In this talk we will discuss reasons of the situation, and how claimed protection has proven ineffective under scrutiny.

Room/Location

Suzhou

Organizer(s)

Houman Homayoun

Description

With the growth and globalization of IC design and development, there is an increase in the number of Designers/Design houses. As setting up a fabrication facility may easily cost upwards of $20 billion, costs for advanced nodes may be even greater. IC design houses that cannot produce their chips in-house have no other option but to make use of external foundries that are often in other countries. Establishing trust with these external foundries can be a challenge, and these foundries are assumed to be untrusted. The use of these untrusted foundries in the global semiconductor supply chain has raised concerns about the security of the fabricated ICs that are targeted for sensitive applications. One of these security threats is the adversarial infestation of fabricated ICs with a Hardware Trojan. A Hardware Trojan (HT) can be broadly described as a malicious modification to a circuit to control, modify, disable, or monitor its logic. Conventional VLSI manufacturing tests and verification methods fall short in detecting HT due to the different and un-modeled nature of these malicious modifications. Current state-of-the-art HT detection methods utilize statistical analysis of various side-channel information collected from ICs, such as power analysis, power supply transient analysis, regional supply current analysis, temperature analysis, wireless transmission power analysis, and delay analysis. To detect HTs, most methods require a trojan-free reference golden IC. A signature from these golden ICs is extracted and used to detect ICs with HTs. However, access to a golden IC is not always feasible. Thus, a novel mechanism for HT detection is sought that does not require the golden IC. Machine Learning (ML) approaches have emerged to be extremely useful to help eliminate the need for a golden IC. Recent works on utilizing ML for HT detection have been shown to be promising in achieving this goal. Thus, in this class, we will explain utilizing ML as a solution to the challenge of HT detection. Additionally, we will describe the Electronic Design Automation (EDA) tool flow for automating ML-assisted HT detection. Moreover, to further discuss the benefits of ML-assisted HT detection solutions, we will demonstrate a Neural Network (NN)-assisted timing profiling method for HT detection. Finally, we will discuss the shortcomings and open challenges of ML-assisted HT detection methods.

Room/Location

Hangzhou

Organizer(s)

Ramesh Karri

Description

Designers use third-party intellectual property (IP) cores and outsource various steps in the integrated circuit (IC) design and manufacturing flow. As a result, security vulnerabilities have been rising. This is forcing IC designers and end users to re-evaluate their trust in ICs. If attackers get hold of an unprotected IC, they can reverse engineer the IC and pirate the IP. Similarly, if attackers get hold of a design, they can insert malicious circuits and backdoors into the design. Unintended design bugs can also result in security weaknesses.
The first part of the class will outline High-Level Design for Trust techniques to prevent these and similar attacks: Locking/Obfuscation and Secure Sourcing of IPs for High-Level Integration. Locking/Obfuscation implements a built-in obfuscation mechanism in ICs to prevent reverse engineering. Secure sourcing can thwart Trojan insertion in 3rd party Intellectual Properties. The second part of the class will discuss hardware security bugs, focusing on the recent common weakness enumeration (CWE) list for hardware design. We will wrap up by pointing out why hardware security is an essential objective from economics, security, and safety aspects and offer a vision of the emerging directions in hardware cybersecurity.

Monday, October 10

Room/Location

Suzhou

Organizer(s)

Weiwen Jiang, Qiang Guan, and Yong Wang

Description

As one of the most popular machine learning algorithms, neural networks have been applied in a wide variety of applications, such as autonomous vehicles, simultaneous translation, and diagnostic medical imaging. With the increasing requirement on analyzing the large-scale data (e.g., 108 pixels for one 3D-CT medical image), neural networks encounter both memory-wall and compute-bound on classical computers. With the extremely high parallelism in representing and processing information, Quantum Computing is promising to address these limitations. But, how to make full use of the powerful quantum computers to accelerate neural networks is still unclear. QuantumFlow, published at Nature Communications last year, is an end-to-end framework to optimize neural networks onto a given quantum processor. Importantly, following the co-design philosophy, the developed quantum neurons in QuantumFlow demonstrate the quantum advantage. Meanwhile, VACSENis an online visualization system which provides the “easy to understand” visualization of the noise status on all available quantum computing nodes, recommends the most robust transpilation of circuit on the selected quantum computing node, and allows the real-time execution for a given quantum algorithm with noise awareness. In this tutorial, we will introduce how to conduct the co-design of neural networks and quantum circuits with QuantumFlow and VACSEN. We will have hands-on experience in implementing the neural network on the quantum circuit. Finally, targeting the near-term quantum computers, we will discuss how to leverage VACSEN to design quantum neural networks in the NISQ-Era. All attendees will leave with code examples that they can use as the backbone implementation to their own projects, and they will have access to VACSEN for the profiling of quantum devices.

Room/Location

Hangzhou

Organizer(s)

Cong (Callie) Hao, Peipei Zhou, and Jingtong Hu

Description

By integrating AI into small embedded systems, we can use the power of billions of devices that we already use in our lives without depending on extra costly equipment. We can build cheaper devices that adapt to our daily lives and have a high impact on how we deal with the environment around us. In this 3-hour tutorial, three speakers will cover the hardware/software co-design of accelerators, performance autotuning in AI chips, and some novel applications.

Room/Location

Yangtze Auditorium

Organizer(s)

Aviral Shrivastava

Description

Room/Location

Yangtze Auditorium

Organizer(s)

Edwin Sha

Description

Big data, AI (Artificial Intelligence), and blockchain become essential for the cyber digital world. The industrial Internet is the use of trusted big data collected from smart sensors and actuators to enhance manufacturing and industrial processes with power of AI and real-time analytics. In this talk, we will overview big data, AI, blockchain, and Industrial Internet. We will address the application and challenge issues in applications of big data, AI, and blockchain for Industrial Internet.

Room/Location

Suzhou

Organizer(s)

Per Gunnar Kjeldsberg

Description

Papers/Talks

  • Adaptive Switch on Wear Leveling for Enhancing I/O Latency and Lifetime of High-density SSDs

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jiaojiao Wu (Southwest University); Jun Li (Southwest University); Zhibing Sha (Southwest University); Zhigang Cai (Southwest University); Jianwei Liao (Southwest University)

  • [Best paper candidate] Horae: A Hybrid I/O Request Scheduling Technique for Near-Data Processing-based SSD

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jiali Li (Chongqing University); Xianzhang Chen (Chongqing University); Duo Liu (Chongqing University); Lin Li (Chongqing University); Jiapin Wang (Chongqing University); Zhaoyang Zeng (Chongqing University); Yujuan Tan (Chongqing University); Lei Qiao (Beijing Institute of Control Engineering)

  • Hybrid RRAM/SRAM In-Memory Computing for Robust DNN Acceleration

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Gokul Krishnan (Arizona State University); Zhenyu Wang (Arizona State University); Injune Yeo (Arizona State University); Li Yang (Arizona State University); Jian Meng (Arizona State University); Maximilian Liehr (State University of New York Polytechnic Institute); Rajiv Joshi (IBM); Nathaniel C. Cady (State University of New York Polytechnic Institute); Deliang Fan (Arizona State University); Jae-Sun Seo (Arizona State University); Yu Cao (Arizona State University)

  • Work-in-Progress: A Processing-in-Pixel Accelerator based on Multi-level HfOx ReRAM

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Minhaz Abedin (SUNY Polytechnic Institute); Arman Roohi (University of Nebraska–Lincoln); Nathaniel Cady (SUNY Polytechnic Institute); Shaahin Angizi (New Jersey Institute of Technology)

Room/Location

Hangzhou

Organizer(s)

Wanli Chang and Fei Wu

Papers/Talks

  • An Efficient BCNN Deployment Method Using Quality-Aware Approximate Computing

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Bo Liu (Southeast University); Hao Cai (Southeast University); Ziyu Wang (Southeast University); Xuetao Wang (Southeast University); Renyuan Zhang (Southeast University); Anfeng Xue (Southeast University); Qiao Shen (Southeast University); Na Xie (Southeast University); Yu Gong (Southeast University); Zhen Wang (Nanjing Prochip Electronic Technology Co. Ltd); Jun Yang (Southeast University); Hao Cai (Southeast University)

  • Memory-Computing Decoupling: A DNN Multitasking Accelerator With Adaptive Data Arrangement

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Chuxi Li (Northwestern Polytechnical University); Xiaoya Fan (Northwestern Polytechnical University); Xiaoti Wu (Northwestern Polytechnical University); Zhao Yang (Northwestern Polytechnical University); Miao Wang (Northwestern Polytechnical University); Meng Zhang (Northwestern Polytechnical University); Shengbing Zhang (Northwestern Polytechnical University)

  • [Best paper candidate] NeuroMap: Efficient Task Mapping of Deep Neural Networks for Dynamic Thermal Management in High Bandwidth Memory

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Shailja Pandey (Indian Institute of Technology Delhi); Preeti Ranjan Panda (Indian Institute of Technology Delhi)

  • On Transferring Application Mapping Knowledge Between Differing MPSoC Architectures

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jan Spieck (Friedrich-Alexander-Universität Erlangen-Nürnberg); Stefan Wildermann (Friedrich-Alexander-Universität Erlangen-Nürnberg); Jürgen Teich (Friedrich-Alexander-Universität Erlangen-Nürnberg)

Room/Location

Zhujiaojiao

Organizer(s)

Heiko Falk

Description

Papers/Talks

  • iNVMFS: An Efficient File System for NVRAM-Based Intermittent Computing Devices

    [Trailer video (YouTube)] [Trailer video (Bilibili)]

    Ying-Jan Wu (National Yang Ming Chiao Tung University); Ching-Yu Kuo (National Yang Ming Chiao Tung University); Li-Pin Chang (National Yang Ming Chiao Tung University)

  • Mercury: Instruction Pipeline Aware Code Generation for Simulink Models

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Zehong Yu (Tsinghua University); Zhuo Su (Tsinghua University); Yixiao Yang (Capital Normal University); Jie Liang (Tsinghua University); Yu Jiang (Tsinghua University); Aiguo Cui (Huawei Technologies Co. Ltd.); Wanli Chang (Hunan University); Rui Wang (Capital Normal University);

  • An I/O Virtualization Framework With I/O-Related Memory Contention Control for Real-Time Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Niccolò Borgioli (Scuola Superiore Sant’Anna); Matteo Zini (Scuola Superiore Sant’Anna); Daniel Casini (Scuola Superiore Sant’Anna); Giorgiomaria Cicero (Scuola Superiore Sant’Anna); Alessandro Biondi (Scuola Superiore Sant’Anna); Giorgio Buttazzo (Scuola Superiore Sant’Anna);

  • Towards Register Spilling Security using LLVM and ARM Pointer Authentication

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Andrea Fanti (Politecnico di Torino); Carlos Chinea Perez (Huawei Technologies Oy Co. Ltd); Remi Denis-Courmont (Huawei Technologies Oy Co. Ltd); Gianluca Roascio (Politecnico di Torino); Jan-Erik Ekberg (Huawei Technologies Oy Co. Ltd);

Room/Location

Organizer(s)

Dirk Ziegenbein

Description

Papers/Talks

  • Industry Paper: Towards Agile Design of Neural Processing Unit

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Binyi Wu (Technische Universität Dresden & Infineon Technologies AG Dresden, Germany); Wolfgang Furtner (Infineon Technologies AG München, Germany); Bernd Waschneck (Infineon Technologies AG Dresden, Germany); Christian Mayr (Infineon Technologies AG Dresden, Germany)

  • Industrial Paper: Challenges in rebooting autonomy with deep learned perception

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Michael Abraham (The Boeing Company, USA); Aaron Mayne (The Boeing Company, USA); Tristan Perez (The Boeing Company, USA); Chiao Hsieh (University of Illinois at Urbana-Champaign UIUC); Yangge Li (University of Illinois at Urbana-Champaign UIUC); Italo Romani De Oliveira (The Boeing Company, USA); Huafeng Yu (The Boeing Company, USA); Dawei Sun (University of Illinois at Urbana-Champaign UIUC); Sayan Mitra (University of Illinois at Urbana-Champaign UIUC)

  • Industry Paper: Surrogate Models for Testing Analog Designs under Limited Budget – a Bandgap Case Study

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Roderick Bloem (Graz University of Technology); Alberto Larrauri (Graz University of Technology); Roland Lengfeldner (Infineon Technologies); Cristinel Mateis (AIT Austrian Institute of Technology); Dejan Nickovic (AIT Austrian Institute of Technology); Bjšrn Ziegler (AIT Austrian Institute of Technology)

Room/Location

The Bund

Room/Location

Shanghai Courtyard

Room/Location

Yu Garden Yoga Studio

Room/Location


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Room/Location

Central Lounge

Room/Location

Grand Ballroom

Room/Location

Suzhou

Organizer(s)

Prabuddha Chakraborty and Wanli Chang

Papers/Talks

  • CMQ: Crossbar-aware Neural Network Mixed-precision Quantization via Differentiable Architecture Search

    [Trailer video (YouTube] [Trailer video (BiliBili)]

    Jie Peng (National University of Defense Technology); Haijun Liu (National University of Defense Technology); Zhongjin Zhao (National University of Defense Technology); Zhiwei Li (National University of Defense Technology); Sen Liu (National University of Defense Technology; Qingjiang Li (National University of Defense Technology)

  • [Best paper candidate] SASCHA – Sparsity-Aware Stochastic Computing Hardware Architecture for Neural Network Acceleration

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Wojciech Romaszkan (University of California at Los Angeles); Tianmu Li (University of California at Los Angeles); Puneet Gupta (University of California at Los Angeles)

  • Work-in-Progress: Ultra-fast yet Accurate Performance Prediction for Deep Neural Network Accelerators

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Konstantin Lübeck (University of Tübingen); Alexander Louis-Ferdinand Jung (University of Tübingen); Felix Wedlich (University of Tübingen); Oliver Bringmann (University of Tübingen)

  • Work-in-Progress: SuperNAS: Fast Multi-objective SuperNet Architecture Search for Semantic Segmentation

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Marihan Amein (Mcgill University); Zhuoran Xiong (McGill University); Olivier Therrien (McGill University); Brett H. Meyer (McGill University); Warren J. Gross (McGill University)

Room/Location

Hangzhou

Organizer(s)

Peipei Zhou and Weiwen Jiang

Papers/Talks

  • Amphis: Managing Reconfigurable Processor Architectures with Generative Adversarial Learning

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Weiwei Chen (Institute of Computing Technology, Chinese Academy of Sciences); Ying Wang (Institute of Computing Technology, Chinese Academy of Sciences); Ying Xu (Institute of Computing Technology, Chinese Academy of Sciences); Chengsi Gao (Institute of Computing Technology, Chinese Academy of Sciences); Yinhe Han (Institute of Computing Technology, Chinese Academy of Sciences); Lei Zhang (Institute of Computing Technology, Chinese Academy of Sciences)

  • Adaptive Mode Transformation for Wear Leveling in Nonvolatile FPGAs

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Huichuan Zheng (Shandong University); Hao Zhang (Shandong University); Shuo Xu (Shandong University); Fanjin Xu (Shandong University); Mengying Zhao (Shandong University)

  • FRL: Fast and Reconfigurable Accelerator for Distributed Sound Source Localization

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Xiaofeng Ding (Chong Qing University); Chengliang Wang (Chongqing University); Heping Liu (Chong Qing University); Zhihai Zhang (Chongqing University); Xianzhang Chen (Chongqing University); Yujuan Tan (Chongqing University); Duo Liu (Chongqing University); Ao Ren (Chongqing University)

Room/Location

Zhujiaojiao

Organizer(s)

Hokeun Kim

Description

Papers/Talks

  • Safety Analysis of Embedded Controllers Under Implementation Platform Timing Uncertainties

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Clara Hobbs (University of North Carolina at Chapel Hill); Bineet Ghosh (University of North Carolina at Chapel Hill); Shengjie Xu (University of North Carolina at Chapel Hill); Parasara Sridhar Duggirala (University of North Carolina at Chapel Hill); Samarjit Chakraborty (University of North Carolina at Chapel Hill)

  • [Best paper candidate] Verified Train Controllers for the Federal Railroad Administration Train Kinematics Model: Balancing Competing Brake and Track Forces

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Aditi Kabra (Carnegie Mellon University); Stefan Mitsch (Carnegie Mellon University); André Platzer (Carnegie Mellon University)

  • Verifying Controllers With Vision-Based Perception Using Safe Approximate Abstractions

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Chiao Hsieh (University of Illinois at Urbana-Champaign); Yangge Li (University of Illinois at Urbana-Champaign); Dawei Sun (University of Illinois at Urbana-Champaign); Keyur Joshi (University of Illinois at Urbana-Champaign); Sasa Misailovic (University of Illinois at Urbana-Champaign); Sayan Mitra (University of Illinois at Urbana Champaign)

Room/Location

Longhua Tower

Organizer(s)

Mohsen Imani (UC Irvine); Xun Jiao (Villanova University); Hussam Amrouch (University of Stuttgart); Yiannis Aloimonos (University of Maryland, College Park)

Description

Papers/Talks

  • Brain-Inspired Hyperdimensional Computing for Ultra-Efficient Edge AI

    Hussam Amrouch (University of Stuttgart); Mohsen Imani (UC Irvine); Xun Jiao (Villanova University); Yiannis Aloimonos (University of Maryland, College Park); Cornelia Fermuller (University of Maryland, College Park); Dehao Yuan (University of Maryland, College Park); Dongning Ma (Villanova University); Hamza Errahmouni (UC Irvine); Paul R. Genssler (University of Stuttgart); Peter Sutor (University of Maryland, College Park); 

Room/Location

Suzhou

Organizer(s)

Partha Pande and Jingtong Hu

Papers/Talks

  • [Best paper candidate] SWAP: A Server-scale Communication-Aware Chiplet-Based Manycore PIM Accelerator

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Harsh Sharma (Washington State University); Sumit K. Mandal (University of Wisconsin-Madison); Janardhan Rao Doppa (Washington State University); Umit Y. Ogras (University of Wisconsin – Madison); Partha Pratim Pande (Washington State University)

  • Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Ourania Spantidi (Southern Illinois University Carbondale); Georgios Zervakis (Karlsruhe Institute of Technology); Iraklis Anagnostopoulos (Southern Illinois University Carbondale); Joerg Henkel (Karlsruhe Institute of Technology)

  • Work-in-Progress: Toward a Robust, Reconfigurable Hardware Accelerator for Tree-Based Genetic Programming

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Christopher Crary (University of Florida); Wesley Piard (University of Florida); Britton Chesley (University of Florida); Greg Stitt (University of Florida)

  • Work-in-Progress: Smart Data Reduction in SLAM Methods for Embedded Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Quentin Picard (CEA, LIST); Stephane Chevobbe (CEA, LIST), Mehdi Darouich (CEA, LIST), Zoe Mandelli (CEA, LIST), Mathieu Carrier (CEA, LIST), Jean-Yves Didier (IBISC, Univ Evry, Université Paris-Saclay)

  • Work-in-Progress: NoRF: A Case Against Register File Operands in Tightly-Coupled Accelerators

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    David J. Schlais (University of Wisconsin-Madison); Heng Zhuo (University of Wisconsin-Madison); Mikko H. Lipasti (University of Wisconsin-Madison)

     

Room/Location

Hangzhou

Organizer(s)

Xiang Chen and Xun Jiao

Papers/Talks

  • PervasiveFL: Pervasive Federated Learning for Heterogeneous IoT Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jun Xia (East China Normal University); Tian Liu (East China Normal University); Zhiwei Ling (East China Normal University); Ting Wang (East China Normal University); Xin Fu (University of Houston); Mingsong Chen (East China Normal University)

  • Optimizing Random Forest Based Inference on RISC-V MCUs at the Extreme Edge

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Enrico Tabanelli (University of Bologna); Giuseppe Tagliavini (University of Bologna); Luca Benini (University of Bologna and ETH Zurich)

  • Enabling Weakly Supervised Temporal Action Localization From On-Device Learning of the Video Stream

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yue Tang (University of Pittsburgh); Yawen Wu (University of Pittsburgh); Peipei Zhou (University of Pittsburgh); Jingtong Hu (University of Pittsburgh)

  • Work-in-Progress: What to Expect of Early Training Statistics? An Investigation on Hardware-Aware Neural Architecture Search

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Xiangzhong Luo (Nanyang Technological University); Di Liu (Yunnan University); Hao Kong (Nanyang Technological University); Shuo Huai (Nanyang Technological University); Hui Chen (Nanyang Technological University); Weichen Liu (Nanyang Technological University)

  • Work-in-Progress: Utilizing Latency and Accuracy Predictors for Efficient Hardware-Aware NAS

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Negin Firouzian (McGill University); Seyyed Hasan Mozafari (McGill University); James J. Clark (McGill University); Warren Gross (McGill University); Brett H. Meyer (McGill University)

  • Work-in-Progress: BloCirNN: An Efficient Software/hardware Codesign Approach for Neural Network Accelerators with Block-Circulant Matrix

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yunji Qin (University of Science and Technology of China); Lei Gong (University of Science and Technology of China); Zhendong Zheng (University of Science and Technology of China); Chao Wang (University of Science and Technology of China)

Room/Location

Zhujiaojiao

Organizer(s)

Chih-Hong Cheng

Description

Papers/Talks

  • Adaptive Edge Offloading for Image Classification Under Rate Limit

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jiaming Qiu (Washington University in Saint Louis); Ruiqi Wang (Washington University in Saint Louis); Ayan Chakrabarti (Google Research); Roch Guérin (Washington University in Saint Louis); Chenyang Lu (Washington University in Saint Louis);

  • Online Rerouting and Rescheduling of Time-Triggered Flows for Fault Tolerance in Time-Sensitive Networking

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Zhiwei Feng (Northeastern University); Zonghua Gu (Umeå University); Haichuan Yu (Northeastern University); Qingxu Deng (Northeastern University); Linwei Niu (Howard University)

  • NExG: Provable and Guided State-Space Exploration of Neural Network Control Systems using Sensitivity Approximation

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Manish Goyal (University of North Carolina at Chapel HIll); Miheer Dewaskar (Duke University); Parasara Sridhar Duggirala (Universty of North Carolina at Chapel Hill)

Tuesday, October 11

Room/Location

The Bund

Room/Location

Shanghai Courtyard

Room/Location

Yu Garden Yoga Studio

Room/Location

Fuxing Park

Room/Location

Yu Garden Yoga Studio

Room/Location

Yangtze Auditorium

Organizer(s)

Karen Zhuge

Description

Electronic Design Automation (EDA) is critical for designing and manufacturing integrated circuits. Recent advancements of AI technologies can help improving the traditional EDA technology and transferring the IC design experiences from old designs to new designs or from old technologies to the new ones. In this talk, we will take a look of a few examples that apply AI to EDA and demonstrate the advantages of such technologies.

Room/Location

Suzhou

Organizer(s)

Liang Shi; Weichen Liu; Yuan-hao Chang

Description

Room/Location

Hangzhou

Organizer(s)

Keni Qiu; Shuaiwen Song

Description

Program:

1) 9:30 am – 10:00 am
Keynote: TBD
Speaker: Ying Wang (ICT-CAS)

2) 10:00 am – 10:25 am
Invited Talk 1: Exploring the Design Space of Future Planet-Scale VR System
Speaker: Shuaiwen Leon Song (Sydney University)

3) 10:25 am – 10:40 am
Regular Presentation 1: Intermittent-Aware Neural Architecture Search
(This work was previously accepted by CODES+ISSS 2021 and has been published in ACM TECS 2021.)
Speaker: Hashan Roshantha Mendis (Research Center for Information Technology Innovation (CITI), Academia Sinica)

4) 10:40 am – 10:55 am
Regular Presentation 2: Keep Fresh: Real-time Data Retrieval with Speed Adaptation in Mobile Cyber-Physical Systems
(This work has been published in RTSS 2021.)
Speaker: Xiaoxing Qiu (Southeast University)

———————————–10:55 am – 11:05 am Break—————————-

5) 11:05 am – 11:30 am
Invited Talk 2: Efficient Scaling of Loosely-Coupled Accelerators for Embedded Computing
Speaker: Shuo Wang (Tsinghua University)

6) 11:30 am – 11:45 am
Regular Presentation 3: EAn efficient associative memory engine for cosine similarity based nearest neighbor search
(This work has been accepted by ICCAD 2022.)
Speaker: Che-Kai Liu (Zhejiang University)

7) 11:45 am – 12:00 am
Regular Presentation 4: Lifetime Improvement through Adaptive Reconfiguration for Nonvolatile FPGAs
(This work has been published in JSA 2022.)
Speaker: Huichuan Zheng (Shandong University)

Room/Location

Zhujiaojiao

Organizer(s)

Chenren Xu; Mingsong Chen

Room/Location


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Room/Location

Suzhou

Organizer(s)

Rajshekar K

Papers/Talks

  • Quantifying Information Leakage for Security Verification of Compiler Optimizations

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Priyanka Panigrahi (Indian Institute of Technology Guwahati); Abhik Paul (Indian Institute of Technology Guwahati); Chandan Karfa (Indian Institute of Technology Guwahati)

  • A Novel Attack Mode on Advanced Technology Nodes Exploiting Transistor Self-Heating

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Nikhil Rangarajan (New York University Abu Dhabi); Johann Knechtel (New York University Abu Dhabi); Nimisha Limaye (New York University); Ozgur Sinanoglu (New York University Abu Dhabi); Hussam Amrouch (University of Stuttgart)

  • Work-in-Progress: Reliability Evaluation of Power SCADA System with Three-Layer IDS

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yenan Chen (Shanghai Jiao Tong University); Linsen Li (Shanghai Jiao Tong University); Zhaoqian Zhu (Shanghai Jiao Tong University); Yue Wu (Shanghai Jiao Tong University)

  • Work-in-Progress: On Evaluation of On-Chip Thermal Covert Channel Attacks

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jiachen Wang (South China University of Technology); Xiaohang Wang (South China University of Technology); Yingtao Jiang (University of Nevada at Las Vegas); Amit Kumar Singh (University of Essex); Letian Huang (University of Electronic Science and Technology of China); Mei Yang (University of Nevada at Las Vegas)

  • Work-in-Progress: Towards a Smaller than Grain Stream Cipher: Optimized FPGA Implementations of Fruit-80

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Gangqiang Yang (Shandong University); Zhengyuan Shi (Shandong University); Cheng Chen (Shandong University); Hailiang Xiong (Shandong University), Honggang Hu (University of Science and Technology of China); Zhiguo Wan (Zhejiang Lab); Keke Gai (Beijing Institute of Technology); Meikang Qiu (Texas A&M University-Commerce)

     

Room/Location

Hangzhou

Organizer(s)

Chen Liu and Wei Jiang

Papers/Talks

  • [Best paper candidate] FLAM-PUF: A Response-Feedback-based Lightweight Anti-Machine-Learning-Attack PUF

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Linjun Wu (Hunan University); Yupeng Hu (Hunan University); Kehuan Zhang (The Chinese University of Hong Kong); Wenjia Li (New York Institute of Technology); Xiaolin Xu (Northeastern University); Wanli Chang (Hunan University and Huawei Technologies)

  • CaPUF: Cascaded PUF Structure for Machine Learning Resiliency

    [Trailer Video (YouTube)] [Trailer Video (BiliBili)]

    Hassan Nassar (Karlsruhe Institute of Technology); Lars Bauer (Karlsruhe Institute of Technology); Joerg Henkel (Karlsruhe Institute of Technology)

  • BLAST: Belling the Black-Hat High-level Synthesis Tool

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Mohammed Abderehman (Indian Institute Of Technology Guwahati); Rupak Gupta (Indian Institute Of Technology Guwahati); Theegala Rakesh Reddy (Indian Institute Of Technology Guwahati); Chandan Karfa (Indian Institute Of Technology Guwahati)

Room/Location

Zhujiaojiao

Organizer(s)

Alessandro Biondi

Papers/Talks

  • [Best paper candidate] Vulnerability Detection of ICS Protocols via Cross-State Fuzzing

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Feilong Zuo (Tsinghua University); Zhengxiong Luo (Tsinghua University); Junze Yu (Tsinghua University); Ting Chen (University of Electronic Science and Technology of China); Zichen Xu (Nanchang University); Aiguo Cui (Huawei Technologies Co. Ltd.); Yu Jiang (Tsinghua University)

  • MIDAS: Safeguarding IoT Devices Against Malware via Real-Time Behavior Auditing

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yiwen Xu (Tsinghua University); Zijing Yin (Tsinghua University); Yiwei Hou (Tsinghua University); Jianzhong Liu (Tsinghua University); Yu Jiang (Tsinghua University)

Room/Location

Organizer(s)

Reiley Jeyapaul

Description

Papers/Talks

  • Work-in-Progress: Toward Energy-Efficient Near STT-MRAM Processing Architecture for Neural Networks

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yueting Li (Beihang University); Bingluo Zhao (Beihang University); Xinyi Xu (Beihang University); Yundong Zhang (Vimicro Corporation); Jun Wang (Beihang University); Weisheng Zhao (Beihang University)

  • Work-in-Progress: HeteroRW: A Generalized and Efficient Framework for Random Walks in Graph Analysis

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yingxue Gao (University of Science and Technology of China); Lei Gong (University of Science and Technology of China); Chao Wang (University of Science and Technology of China); Xuehai Zhou (University of Science and Technology of China)

  • Work-in-Progress: Scheduler for Collaborated FPGA-GPU-CPU Based on Intermediate Language

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Nan Hu (University of Science and Technology of China); Chao Wang (University of Science and Technology of China); Xuehai Zhou (University of Science and Technology of China); Xi Li (University of Science and Technology of China)

  • Work-in-Progress: High-Performance Systolic Hardware Accelerator for RBLWE-based Post-Quantum Cryptography

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Tianyou Bao (Villanova University); José L. Imaña (Complutense University); Pengzhou He (Villanova University); Jiafeng Xie (Villanova University)

  • Work-in-Progress: Lark: A Learned Secondary Index Toward LSM-tree for Resource-Constrained Embedded Storage Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jianan Yuan (Shenzhen University); Huan Liu (Shenzhen University); Shangyu Wu (City University of Hong Kong); Yiquan Lin (Shenzhen University); Tiantian Wang (Shenzhen University); Chenlin Ma (Shenzhen University); Rui Mao (Shenzhen University); Yi Wang (Shenzhen University)

Room/Location

Suzhou

Organizer(s)

Wei Zhang

Papers/Talks

  • Secured Data Transmission over Insecure Network-on-Chip by Modulating Inter-Packet Delays

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jiaen Xu (South China University of Technology); Xiaohang Wang (Zhejiang University); Yingtao Jiang (University of Nevada at Las Vegas); Amit Kumar Singh (University of Essex); Chongyan Gu (Queen’s University Belfast); Mei Yang (University of Nevada at Las Vegas); Letian Huang (School of Electronic Science and Engineering); Shunbin Li (Zhejiang Lab)

  • GCIM: Towards Efficient Processing of Graph Convolutional Networks in 3D-Stacked Memory

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jiaxian Chen (Shenzhen University); Yiquan Lin (Shenzhen University); Kaoyi Sun (Shenzhen University); Jiexin Chen (Shenzhen University); Chenlin Ma (Shenzhen University); Rui Mao (Shenzhen University); Yi Wang (Shenzhen University)

  • Accelerating Large-Scale Graph Neural Network Training on Crossbar Diet

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Chukwufumnanya Ogbogu (Washington State University); Aqeeb Iqbal Arka (Washington State University); Biresh Kumar Joardar (Duke University); Janardhan Rao Doppa (Washington State University); Hai (Helen) Li (Duke University); Krishnendu Chakrabarty (Duke University); Partha Pratim Pande (Washington State University)

  • Work-in-Progress: RISC-V Based Low-Cost Embedded Trace Processing System

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Xiao Hu (National University of Defense Technology); Yaohua Wang (National University of Defense Technology); Xuan Gao (National University of Defense Technology)

  • Work-in-Progress: MLGOPerf: An ML Guided Inliner to Optimize Performance

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Amir H. Ashouri (Huawei Technologies); Mostafa Elhoushi (Huawei Technologies); Yuzhe Hua (Huawei Technologies); Xiang Wang (Huawei Technologies); Muhammad Asif Manzoor (Huawei Technologies); Bryan Chan (Huawei Technologies); Yaoqing Gao (Huawei Technologies)

Room/Location

Hangzhou

Organizer(s)

Liang Shi and Ming-chang Yang

Papers/Talks

  • [Best paper candidate] Exploring Synchronous Page Fault Handling

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yin-Chiuan Chen (National Taiwan University); Chun-Feng Wu (National Yang Ming Chiao Tung University and Harvard University); Yuan-Hao Chang (Academia Sinica); Tei-Wei Kuo (City University of Hong Kong and National Taiwan University)

  • Resolving the Reliability Issues of Open Blocks for 3-D NAND Flash: Observations and Strategies

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Qiao Li (Xiamen University); Min Ye (City University of Hong Kong); Yufei Cui (City University of Hong Kong); Tianyu Ren (City University of Hong Kong); Tei-Wei Kuo (National Taiwan University); Jason Xue (City University of Hong Kong)

  • WA-OPShare: Workload-Adaptive Over-Provisioning Space Allocation for Multi-tenant SSDs

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yuhong Wen (Huazhong University of Science and Technology); You Zhou (Huazhong University of Science and Technology); Fei Wu (Huazhong University of Science and Technology); Shu Li (Alibaba Group); Zhenghong Wang (Alibaba Group); ChangSheng Xie (Huazhong University of Science and Technology)

Room/Location

Zhujiaojiao

Organizer(s)

Alain Girault

Papers/Talks

  • Throughput Maximization in Wireless Communication Systems Powered by Hybrid Energy Harvesting

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Chenchen Fu (Southeast University); Xinhang Lu (The University of New South Wales); Xiaoxing Qiu (Southeast University); Sujunjie Sun (Southeast University); Xueyong Xu (North Information Control Research Academy Group Co., Ltd); Weiwei Wu (Southeast University); Chun Jason Xue (City University of Hong Kong); Song Han (University of Connecticut)

  • Work-in-Progress: Accuracy-Area Efficient Online Fault Detection for Robust Neural Network Software-Embedded Microcontrollers

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Juneseo Chang (Seoul National University); Sejong Oh (Nvidia); Daejin Park (Kyungpook National University)

  • Work-in-Progress: Accelerated Matrix Factorization by Approximate Computing for Recommendation System

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yining Wu (Shanghai University); Gaole Sai (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences); Shengyu Duan (Shanghai University)

Room/Location

The Bund

Room/Location

Shanghai Courtyard

Room/Location


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Room/Location

Yu Garden Yoga Studio

Room/Location

Fuxing Park

Room/Location

Yu Garden Yoga Studio

Room/Location

Central Lounge

Room/Location

Yangtze Auditorium

Organizer(s)

Jürgen Teich

Room/Location

Yangtze Auditorium

Organizer(s)

Aviral Shrivastava

Description

Automobile racing represents an extreme example of real-time decision making in complex physical environments. Drivers must execute complex tactical maneuvers to pass or block opponents while operating their vehicles at their traction limits. Modern racing simulations, such as the PlayStation game Gran Turismo, faithfully reproduce much of the nonlinear control challenges of real race cars while also encapsulating the complex multi-agent interactions. In this talk I will describe how our team at Sony AI trained agents for Gran Turismo that can compete with the world’s best e-sports drivers. We combine state-of-the-art model-free deep reinforcement learning algorithms with mixed scenario training to learn an integrated control policy that combines exceptional speed with impressive tactics. In addition, we construct a reward function that enables the agent to be competitive while adhering to racing’s important, but under-specified, sportsmanship rules. We demonstrate the capabilities of our agent, Gran Turismo Sophy, by winning a head-to-head competition against four of the world’s best Gran Turismo drivers.

Room/Location

Suzhou

Organizer(s)

Jana Doppa

Papers/Talks

  • GNN4REL: Graph Neural Networks for Predicting Circuit Reliability Degradation

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Lilas Alrahis (New York University Abu Dhabi); Johann Knechtel (New York University Abu Dhabi); Florian Klemme (University of Stuttgart); Hussam Amrouch (University of Stuttgart); Ozgur Sinanoglu (New York University Abu Dhabi)

  • Work-in-Progress: ACAC: An Adaptive Congestion-Aware Approximate Communication Mechanism for Network-on-Chip Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Shize Zhou (Nanjing University), Yongqi Xue (Nanjing University), Siyue Li (Nanjing University), Jinlun Ji (Nanjing University); Tong Cheng (Nanjing University), Li Li (Nanjing University), Yuxiang Fu (Nanjing University)

  • Work-in-Progress: CAMiSE: Content Addressable Memory-integrated Searchable Encryption

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Arnab Bag (Indian Institute of Technology Kharagpur); Sikhar Patranabis (ETH Zurich); Debdeep Mukhopadhyay (Indian Institute of Technology Kharagpur)

  • Work-in-Progress: An Open-Source Platform for Design and Programming of Partially Reconfigurable Heterogeneous SoCs

    Biruk Seyoum (Columbia University); Davide Giri (Columbia University); Kuan-Lin Chiu (Columbia University); Luca Carloni (Columbia University)

Room/Location

Hangzhou

Organizer(s)

Jiafeng Xie and Ishan Thakkar

Papers/Talks

  • Detecting Spoofed Speeches via Segment-Based Word CQCC and Average ZCR for Embedded Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jinyu Zhan (University of Electronic Science and Technology of China); Zhibei Pu (University of Electronic Science and Technology of China); Wei Jiang (University of Electronic Science and Technology of China); Junting Wu (University of Electronic Science and Technology of China); Yongjia Yang (University of Electronic Science and Technology of China)

  • Combating Stealthy Thermal Covert Channel Attack With Its Thermal Signal Transmitted in Direct Sequence Spread Spectrum

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Xiaohang Wang (Zhejian University and South China University of Technology); Shengjie Wang (South China University of Technology); Yingtao Jiang (University of Nevada Las Vegas); Amit Kumar Singh (University of Essex); Mei Yang (University of Nevada Las Vegas); Letian Huang (University of Electronic Science and Technology of China)

  • eRDAC: Efficient and Reliable Remote Direct Access and Control for Embedded Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Junjie Feng (Chongqing University); Xianzhang Chen (Chongqing University); Duo Liu (Chongqing University); Weigong Zhang (Capital Normal University); Jiapin Wang (Chongqing University); Rongwei Zheng (Chongqing university); Yujuan Tan (Chongqing University)

  • Architecting Decentralization and Customizability in DNN Accelerators for Hardware Defect Adaptation

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Elbruz Ozen (University of California at San Diego); Alex Orailoglu (University of California at San Diego)

Room/Location

Zhujiaojiao

Organizer(s)

Susanne Graf

Papers/Talks

  • CapOS: Capacitor Error Resilience for Energy Harvesting Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jongouk Choi (University of Central Florida); Hyunwoo Joe (Electronics and Telecommunications Research Institute (ETRI)); Changhee Jung (Purdue University)

  • Sparsity-Aware Intelligent Spatiotemporal Data Sensing for Energy Harvesting IoT System

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Wen Zhang (Texas A&M University at Corpus Christi); Mimi Xie (The University of Texas at San Antonio); Caleb Scott (The University of Texas at San Antonio); Chen Pan (Texas A&M University at Corpus Christi)

  • Work-in-Progress: Boot Sequence Integrity Verification with Power Analysis

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Arthur Grisel-Davy (University of Waterloo); Amrita Milan Bhogayata (University of Waterloo); Srijan Pabbi (University of Waterloo); Apurva Narayan (University of Waterloo); Sebastian Fischmeister (University of Waterloo)

  • Work-in-Progress: Towards a Theory of Robust Quantitative Semantics for Signal Temporal Logic

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jean-Baptiste Jeannin (University of Michigan); Jiawei Chen (University of Michigan); José Luiz Vargas de Mendonça (University of Michigan); Konstantinos Mamouras (Rice University)

Room/Location

Longhua Tower

Organizer(s)

Shaoshan Liu (PerceptIn); Tongsheng Geng (UC Irvine); Stéphane Zuckerman (ETIS); Xiaoming Li (University of Delaware); Jean-Luc Gaudiot (UC Irvine)

Description

Papers/Talks

  • Programming Autonomous Machines

    Shaoshan Liu (PerceptIn); Xiaoming Li (University of Delaware); Tongsheng Geng (UC Irvine); Stéphane Zuckerman (ETIS); Jean-Luc Gaudiot (UC Irvine)

Wednesday, October 12

Room/Location

The Bund

Room/Location

Shanghai Courtyard

Room/Location

Yu Garden Yoga Studio

Room/Location

Yangtze Auditorium

Organizer(s)

Liang Shi

Description

AMD/Xilinx’s embedded SoCs integrate a lot of heterogenous execution units, like multiple CPU clusters, AI Engines, programmable logic and other accelerators. Most traditional embedded software stacks are optimized for a single operating environment running on one or more CPUs and need to be extended to handle the new complexity. This talk will introduce the Xilinx SoCs and the various open source projects that handles both the runtimes (communication, management, separation, etc.) and tooling (compilation, configuration, debugging, etc.) for these execution units.

Room/Location

Suzhou

Organizer(s)

Liang Shi; Weichen Liu; Yuan-hao Chang

Room/Location

Hangzhou

Organizer(s)

Songwen Pei; Tong Liu

Room/Location

Longhua Tower

Organizer(s)

Yuan-Hao Chang

Room/Location

Suzhou

Organizer(s)

Duo Liu and Sandeep Chandran

Papers/Talks

  • LLSM: A Lifetime-Aware Wear-Leveling for LSM-Tree on NAND Flash Memory

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Dharamjeet Dharamjeet (Academia Sinica); Yi-Shen Chen (Academia Sinica); Tseng-Yi Chen (National Central University); Yuan-Hung Kuan (Academia Sinica); Yuan-Hao Chang (Academia Sinica)

  • Fast and Low Overhead Metadata Operations for NVM-Based File System Using Slotted Paging

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Fangzhu Lin (Chongqing University); Chunhua Xiao (Chongqing University); Weichen Liu (Nanyang Technological University); Lin Wu (Chongqing University); Chen Shi (Chongqing University); Kun Ning(Chongqing University)

  • MAID-Q: Minimizing Tail Latency in Embedded Flash With SMR Disk via Q-learning Model

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Chenlin Ma (Shenzhen University); Zhuokai Zhou (Shenzhen University); Yingping Wang (Shenzhen University); Yi Wang (Shenzhen University); Rui Mao (Shenzhen University)

  • Work-in-Progress: High-Precision Short-Term Lifetime Prediction in TLC 3D NAND Flash Memory as Hot-data Storage

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Xiaotong Fang (Shandong University); Meng Zhang (Huazhong University of Science and Technology); Yifan Guo (Shandong University); Fei Chen (Shandong University); Binglu Chen (Shandong University); Xuepeng Zhan (Shandong University); Jixuan Wu (Shandong University); Fei Wu (Huazhong University of Science and Technology); Jiezhi Chen (Shandong University)

  • Work-in-Progress: Prediction-based Fine-Grained LDPC Reading to Enhance High-Density Flash Read Performance

    Yajuan Du (Wuhan University of Technology); Yuan Gao (Wuhan University of Technology); Qiao Li (Xiamen University)

  • Work-in-Progress: ExpCache: Online-Learning Based Cache Replacement Policy for Non-Volatile Memory

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jinfeng Yang (University of Minnesota); Bingzhe Li (Oklahoma State University); Jianjun Yuan (Expedia Group); Zhaoyan Shen (Shandong University); David Du (University of Minnesota); David Lilja (University of Minnesota)

Room/Location

Hangzhou

Organizer(s)

Yuan-Hao Chang and Mengying Zhao

Papers/Talks

  • Stateful Neural Networks for Intermittent Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Chih-Hsuan Yen (National Taiwan University and Academia Sinica); Hashan Roshantha Mendis (Academia Sinica); Tei-Wei Kuo (National Taiwan University); Pi-Cheng Hsiu (Academia Sinica)

  • Intermittent-Aware Distributed Concurrency Control

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Wei-Che Tsai (National Taiwan University); Wei-Ming Chen (Massachusetts Institute of Technology); Tei-Wei Kuo (National Taiwan University); Pi-Cheng Hsiu (Academia Sinica)

  • PVoT: Reconfigurable Photovoltaic Array for Indoor Light Energy-Powered Batteryless Devices

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jiwon Kim (Yonsei University); Eunyeong Kim (Yonsei University); Seunghyeok Jeon (Yonsei univ.); JunIck Ahn (Yonsei University); Hyungchol Jun (Yonsei University); Hojung Cha (Yonsei University)

  • SENTunnel: Fast Path for Sensor Data Access on Automotive Embedded Systems

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Rongwei Zheng (Chongqing University); Xianzhang Chen (Chongqing University); Duo Liu (Chongqing University); Junjie Feng (Chongqing University); Jiapin Wang (Chongqing University); Ao Ren (Chongqing University); Chengliang Wang (Chongqing University); Yujuan Tan (Chongqing University)

Room/Location

Zhujiaojiao

Organizer(s)

Indranil Saha

Papers/Talks

  • An MILP Encoding for Efficient Verification of Quantized Deep Neural Networks

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Samvid Mistry (GitHub); Indranil Saha (Indian Institute of Technology Kanpur); Swarnendu Biswas (Indian Institute of Technology Kanpur)

  • Efficient Complete Verification of Neural Networks via Layerwised Splitting and Refinement

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Banghu Yin (National University of Defense Technology); Liqian Chen (National University of Defense Technology); Jiangchao Liu (Ant Group); Ji Wang (National University of Defense Technology)

  • Tardis: Coverage-Guided Embedded Operating System Fuzzing

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yuheng Shen (Tsinghua University); Yiru Xu (Tsinghua University); Hao Sun (Tsinghua University); Jianzhong Liu (Tsinghua University); Zichen Xu (Nanchang University); Aiguo Cui (Huawei Technologies Co. Ltd.); Heyuan Shi (Central South University); Yu Jiang (Tsinghua University)

  • Formal Verification of Resource Synchronization Protocol Implementations: A Case Study in RTEMS

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Junjie Shi (TU Dortmund); Christoph-Cordt von Egidy (TU Dortmund); Kuan-Hsun Chen (University of Twente); Jian-Jia Chen (TU Dortmund)

Room/Location

Longhua Tower

Organizer(s)

Yuan-Hao Chang

Room/Location

Suzhou

Organizer(s)

Neetu Jindal

Papers/Talks

  • Bits Ensemble: Toward Light-Weight Robust Deep Ensemble by Bits Sharing

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yufei Cui (McGill University); Shangyu Wu (City University of Hong Kong); Qiao Li (Xiamen University); Antoni Bert Chan (City University of Hong Kong); Tei-Wei Kuo (National Taiwan University); Chun Jason Xue (City University of Hong Kong)

  • Exploring Bitslicing Architectures for Enabling FHE-Assisted Machine Learning

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Soumik Sinha (Indian Institute of Technology Kharagpur); Sayandeep Saha (Nanyang Technological University,); Manaar Alam (New York University, Abu Dhabi); Varun Agarwal (Delhi Technological University); Ayantika Chatterjee (Indian Institute of Technology Kharagpur); Anoop Mishra (University of Nebraska Omaha); Deepak Khazanchi (University of Nebraska Omaha); Debdeep Mukhopadhyay (Indian Institute of Technology Kharagpur)

  • Work-in-Progress: Object Detection Acceleration Method by Improving Execution Efficiency of AI Device

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yoshikazu Watanabe (NEC Corporation); Yuki Kobayashi (NEC Corporation); Noboru Nakajima (NEC Corporation); Takashi Takenaka (NEC Corporation); Hiroyoshi Miyano (NEC Corporation)

  • Work-in-Progress: Cooperative MLP-Mixer Networks Inference On Heterogeneous Edge Devices through Partition and Fusion

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yiming Li (East China Normal University); Shouzhen Gu (East China Normal University); Mingsong Chen (East China Normal University)

Room/Location

Hangzhou

Organizer(s)

Keni Qiu and Duo Liu

Papers/Talks

  • A Flexible Yet Efficient DNN Pruning Approach for Crossbar-Based Processing-in-Memory Architectures

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Long Zheng (Huazhong University of Science and Technology); Haifeng Liu (Huazhong University of Science and Technology); Yu Huang (Huazhong University of Science and Technology); Dan Chen (Huazhong University of Science and Technology); Chaoqiang Liu (Huazhong University of Science and Technology); Haiheng He (Huazhong University of Science and Technology); Xiaofei Liao (Huazhong University of Science and Technology); Hai Jin (Huazhong University of Science and Technology); Jingling Xue (University of New South Wales)

  • ReaDy: A ReRAM-Based Processing-in-Memory Accelerator for Dynamic Graph Convolutional Networks

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Yu Huang (Huazhong University of Science and Technology); Long Zheng (Huazhong University of Science and Technology); Pengcheng Yao (Huazhong University of Science and Technology); Qinggang Wang (Huazhong University of Science and Technology); Haifeng Liu (Huazhong University of Science and Technology); Xiaofei Liao (Huazhong University of Science and Technology); Hai Jin (Huazhong University of Science and Technology); Jingling Xue (University of New South Wales)

  • Efficient Hardware Acceleration of Sparsely Active Convolutional Spiking Neural Networks

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Jan Sommer (Friedrich-Alexander-Universität Erlangen-Nürnberg); M. Akif Özkan (Friedrich-Alexander-Universität Erlangen-Nürnberg); Oliver Keszocze (Friedrich-Alexander-Universität Erlangen-Nürnberg); Jürgen Teich (Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • ViA: A Novel Vision-Transformer Accelerator Based on FPGA

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Teng Wang (University of Science and Technology of China); Lei Gong (University of Science and Technology of China); Chao Wang (University of Science and Technology of China); Yang Yang (University of Science and Technology of China); Yingxue Gao (University of Science and Technology of China); Xuehai Zhou (University of Science and Technology of China); Huaping Chen (University of Science and Technology of China)

Room/Location

Zhujiaojiao

Organizer(s)

David Broman

Papers/Talks

  • Efficient Backward Reachability Using the Minkowski Difference of Constrained Zonotopes

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Liren Yang (Huazhong University); Hang Zhang (University of Wisconsin-Madison); Jean-Baptiste Jeannin (University of Michigan); Necmiye Ozay (University of Michigan)

  • Toward Minimum WCRT Bound for DAG Tasks Under Prioritized List Scheduling Algorithms

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Shuangshuang Chang (Northeastern University); Ran Bi (Dalian University of Technology); Jinghao Sun (Dalian University of Technology); Weichen Liu (Nanyang Technological University); Qi Yu (Dalian University of Technology); Qingxu Deng (Northeastern University); Zonghua Gu (Umeå University)

  • Response-Time Analysis of Limited-Preemptive Sporadic DAG Tasks

    [Trailer video (YouTube)] [Trailer video (BiliBili)]

    Gaoyang Dai (Uppsala University); Morteza Mohaqeqi (Uppsala University); Petros Voudouris (Uppsala University); Wang Yi (Uppsala University)

Room/Location

Organizer(s)

Mengying Zhao and Tosiron Adegbija

Papers/Talks

Room/Location

The Bund

Room/Location

Shanghai Courtyard

Room/Location

Yu Garden Yoga Studio

Room/Location

Fuxing Park

Room/Location

Central Lounge

Room/Location

Yu Garden Yoga Studio

Room/Location

Yangtze Auditorium

Organizer(s)

MargXiaobo Sharon Huaret Martonosi

Description

The United States National Science Foundation (NSF) supports a majority of US academic research in the Computer and Information Science and Engineering (CISE) topic areas. A long-time computing researcher herself, Dr. Margaret Martonosi is now serving a 4-year term leading the NSF CISE Directorate, and stewarding the CISE directorate’s $1B+ annual budget on behalf of research, education, workforce and infrastructure funding in CISE topic areas and for science as a whole. In this talk, she will discuss key technical themes for the field, and how CISE is developing programmatic opportunities to advance research related to them. She will particularly note how ESWEEK topic areas relate to these technical priorities. More broadly, she will discuss CISE and NSF in the context of the global research efforts, and our approach to industry and international research partnerships.

Room/Location

Yangtze Auditorium

Organizer(s)

Jürgen Teich

Room/Location

Yangtze Auditorium

Organizer(s)

Puneet Gupta (UCLA); Saptadeep Pal (Auradine Inc.)

Description

Title: Waferscale Computing Systems: Are We There Yet?

Panelists: 

Rakesh Kumar is a Professor in the Electrical and Computer Engineering Department at the University of Illinois at Urbana Champaign with research and teaching interests in computer architecture and system-level design automation. His research has been recognized through one ISCA Influential Paper Award, one MICRO Test-of-Time Award, one ASPDAC 10 Year Retrospective Most Influential Paper (MIP) Award, several best paper awards and best paper award nominations (IEEE MICRO Top Picks, ASPLOS, HPCA, CASES, SELSE, IEEE CAL), ARO Young Investigator Award, and UCSD CSE Best Dissertation Award. His teaching and advising have been recognized through Stanley H Pierce Faculty Award and Ronald W Pratt Faculty Outstanding Teaching Award. He often writes about issues at the intersection of technology, policy, and society; he is the author of the book Reluctant Technophiles (Sage Select: Dec 2021), one of “GQ’s Best Indian Non-fiction Books of 2021”. Rakesh has a BS from IIT Kharagpur and a PhD from University of California at San Diego.

 

Gabriel H. Loh is a Senior Fellow in AMD Research, the research and advanced development lab for Advanced Micro Devices, Inc. Gabe received his Ph.D. and M.S. in computer science from Yale University in 2002 and 1999, respectively, and his B.Eng. in electrical engineering from the Cooper Union in 1998. Gabe was also a tenured associate professor in the College of Computing at the Georgia Institute of Technology, a visiting researcher at Microsoft Research, and a senior researcher at Intel Corporation. He is a Fellow of the ACM and IEEE, recipient of ACM SIGARCH’s Maurice Wilkes Award, Hall of Fame member for the MICRO, ISCA, and HPCA conferences, (co-)inventor on over one hundred US patent applications and over ninety granted patents, and a recipient of the US National Science Foundation Young Faculty CAREER Award.

 

Joel Hestness is a Senior Research Scientist at Cerebras Systems, an AI-focused hardware startup building the largest ever processors using wafer-scale integration. Joel helps define algorithms, performance optimizations, and scaling approaches for machine learning and NLP applications on the Cerebras Wafer-Scale Engine. Previously, Joel was a Research Scientist at Baidu’s Silicon Valley AI Lab (SVAIL), where he worked on deep learning speech and language modeling. His work was the first to demonstrate predictable accuracy scaling laws for modern deep learning algorithms, sparking a trend of scaling law studies now pervasive in the field. Joel received his PhD in computer architecture from the University of Wisconsin – Madison, and his Bachelor’s degrees in Mathematics and Computer Science also from UW-Madison. 

 

Dave Nellans joined NVIDIA in 2013 where he is the Director of System Architecture Research.  His research interests include building scalable computing systems that optimize node-level efficiency by improving the performance, utilization, and interaction of GPUs, CPUs, smart NICS, and storage systems. Dr. Nellans was previously an early engineering leader at Fusion-IO, one of the pioneers in PCIe-attached NAND-flash storage, where helped the company invent, develop, and ship new datacenter storage products that ultimately led to Fusion-IO’s IPO in 2011.  He holds a B.A. in Computer Science from Colgate University and a Ph.D. in Computer Science from the University of Utah.

Thursday, October 13

Room/Location

Yangtze Auditorium

Room/Location

Suzhou

Room/Location

Hangzhou

Description

Indicated time is in CET (Europe) time zone

Papers/Talks

  • 16:50 – Welcome and Opening Remarks

  • 17:00 – 17:45 – Session 1 – Application-specific designs for vision

    • Dominique Heller (Lab-STICC/UBS), Mostafa Rizk (Lab-STICC/IMT Atlantique), Ronan Douguet (Lab-STICC/UBS), Amer Baghdadi (IMT Atlantique/Lab-STICC), Jean-Philippe Diguet (IRL CROSSING/CNRS)
      Marine Objects Detection Using Deep Learning on Embedded Edge Devices
      [Teaser video]
    • Seungyeop Kang (Seoul National University), Sungjoo Yoo (Seoul National University)
      TernaryNeRF: Quantizing Voxel Grid-based NeRF Models
      [Teaser video]
    • Zijie Ning ((IMT Atlantique/Lab-STICC)), Mostafa Rizk (Lab-STICC/IMT Atlantique), Amer Baghdadi (IMT Atlantique/Lab-STICC), Jean-Philippe Diguet (IRL CROSSING/CNRS)
      Enhancing Embedded AI-based Object Detection Using Multi-view Approach
      [Teaser video]

  • 17:45 – 18:00 –  Break

  • 18:00 – 18:45 – Session 2 – Processors and systems

    • Weiyan Zhang (DFKI GmbH), Mehran Goli (DFKI GmbH, University of Bremen), Alireza Mahzoon (University of Bremen), Rolf Drechsler (DFKI GmbH, University of Bremen)
      ANN-based Performance Estimation of Embedded Software for RISC-V Processors
      [Teaser video]
    • Arthur Vianès (TIMA/University Grenoble-Alpes), Frédéric Pétrot (TIMA/University Grenoble-Alpes), Frédéric Rousseau (TIMA/University Grenoble-Alpes), Benoît Dupont de Dinechin (Kalray)
      A Case for Second-Level Software Cache Coherency on Many-Core Accelerators
      [Teaser video]
    • Martim Rosado (CERN, Universidade de Lisboa), Stavros Mallios (CERN), Pedro Tomás (Universidade de Lisboa), Nuno Roma (Universidade de Lisboa), André David (CERN)
      Early Prototyping and Testing of CERN LHC CMS High-Granularity Calorimeter Slow-Control System
      [Teaser video]

  • 18:45 – 19:00 – Break

  • 19:00 – 19:45 – Session 3 – Analysis and trade-offs

    • Jakob Wenzel (Technische Universität Darmstadt), Christian Hochberger (Technische Universität Darmstadt)
      Automatically Restructuring HDL Modules for Improved Reusability in Rapid Synthesis
      [Teaser video]
    • Gabriel Rutsch (Infineon Technologies AG), Maximilian Groebner (Infineon Technologies AG), Anthony Sanders (Infineon Technologies AG), Konrad Maier (Infineon Technologies AG), Wolfgang Ecker (Infineon Technologies AG)
      A Framework That Enables Systematic Analysis of Mixed-Signal Applications on FPGA
      [Teaser video]
    • Ritwik Sinha (Hochschule Bonn-Rhein-Sieg), Seyed Damghani (University of New Brunswick), Kenneth Kent (University of New Brunswick)
      Machine Learning Based Hard/Soft Logic Trade-offs in VTR
      [Teaser video]

  • 19:45 – 20:00 – Break – End of Workshop

Room/Location

Longhua Tower

Friday, October 14

Room/Location

Zhujiaojiao

Room/Location

Yangtze Auditorium

Room/Location

Suzhou

Room/Location

Hangzhou

Room/Location

Zhujiaojiao