Tiny and Fair ML Design Contest at ESWEEK 2023

Machine learning (ML) has become increasingly popular in recent years, due to its ability to make predictions or decisions based on the data. One trend in the field of machine learning for embedded systems is the development of more lightweight and efficient algorithms that can run on devices with limited computational resources. The Tiny and Fair ML Design Contest at ESWEEK 2023 features two exciting tracks: Segmentation and Classification, requiring the participants to implement ML algorithm on edge devices. The contest will run for several months and be open to multi-person teams world-wide. The winning teams will be announced and awarded at the ESWEEK.

Call For Participation

Segmentation Track: Low-Power Computer Vision Challenge
Participants will devise models to improve semantic segmentation on an edge device (NVIDIA Jetson Nano 2GB Developer Kit) with a new disaster-scene dataset containing 1,700 samples collected by UAVs.

Classification Track: Fair and Intelligent Embedded System Challenge
Participants will design and implement a working, open-source AI/ML algorithm that can automatically discriminate skin disease from skin image while being able to be deployed and run on the given platform. The prizes will be awarded to the teams with top comprehensive performances in terms of detection accuracy, fairness, and inference latency.


Competition Chair:
Weiwen Jiang, George Mason University

Segmentation Track:
Yung-Hsiang Lu, Purdue University
Ping Hu, Boston University
Gowri Ramshankar, Purdue University
Kate Saenko, Boston University
Nicholas Synovic, Loyola University Chicago
George K. Thiruvathukal, Loyola University Chicago

Classification Track:
Lei Yang, George Mason University
Qian Lou, University of Central Florida
Junhuan Yang, George Mason University
Yi Sheng, George Mason University