CODAI’23: Workshop on Compilers, Deployment, and Tooling for Edge AI

Workshop Webpage

Please visit the CODAI’23 website for more details.


The goal of the CODAI’23 workshop is to bring together the emerging energy in the AI compiler communities and AI accelerator communities focusing on Edge AI in both academic and industrial research. These realms of research have the opportunity to deliver a pervasive and seamless end-to-end tooling that connects hardware and software development methodologies. This workshop mainly focuses on:

  • Discussing new impulses for deployment of neural networks on edge devices.
  • Cooperative development of (open source) toolchains/frameworks for neural network deployment + cooperation between industry and academia.

Workshop Program

Introduction & Keynote
10:00 – 10:30 Welcome Coffee
10:30 – 10:35 Opening
10:35 – 11:15 Keynote: Next-generation Compilers for Emerging Systems
Session 1: Deployment and Optimization Techniques
11:15 – 11:40 Scaling Up Quantization-Aware Neural Architecture Search for Efficient Deep Learning on the Edge
11:40 – 12:05 Tiny Machine Learning: Enabling Intelligence on Constrained Devices
12:05 – 12:30 Hardware-Aware Network Compression: From Data to Silicon
12:30 – 13:30 Lunch Break
Session 2: Compilation Frameworks and Techniques
13:30 – 13:55 Accelerating Edge AI with Morpher: An Integrated Design, Compilation and Simulation Framework for CGRAs
13:55 – 14:20 Towards Rapid Exploration of Heterogeneous TinyML Systems using Virtual Platforms and TVM’s UMA
14:20 – 14:45 ART: An Actor transition systems RunTime for enabling efficient partitioning of neural network graphs
14:45 – 15:10 SYCL – A Modern C++ Programming Model for Accelerators
15:10 – 15:40 Coffee Break
Session 3: Applications
15:40 – 16:05 Temporal Patience: Efficient Adaptive Deep Learning for Embedded Radar Data Processing
16:05 – 16:30 Pros and Cons of Executable Neural Networks for Deeply Embedded Systems
16:30 – 16:55 Software and Hardware for Sparse ML
16:55 – 17:00 Closing remarks


Michael J. Klaiber
EnCharge AI, USA

Sebastian Vogel
NXP Semiconductors, The Netherlands

Dayane Reis
University of South Florida, USA

Andreas Bytyn
Axelera AI, Germany

Dennis Rieber
Bosch Research, Germany

Miguel Aguilar
Aptiv, Germany