The Efficient circuits & systems for Machine Intelligence (EMI) research lab focuses on designing energy-efficient digital AI hardware (ASIC & FPGA) for edge computing, emphasizing ultrahigh-speed communication, video/audio processing, robotics, and biomedical applications. 

We strongly believe that hardware-software co-design is the path to realizing the ultimate goal of intelligent edge solutions for complex real-world applications.

New master projects are available!

August 24, 2023

We are glad to announce that our paper "3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network" has been accepted at the 2023 IEEE BioCAS conference. The paper introduces the Efficient Event-based Eye Tracking (3ET) system that employs a novel temporally sparse change-based ConvLSTM architecture to effectively extract sparse spatio-temporal features from event camera input streams in eye-tracking applications. Our network achieved over 30% better accuracy than CNN-based models and reduced computational operations by 4.7X compared to standard ConvLSTM models on event-based pupil datasets. This marks the first step of the EMI lab in building energy-efficient neuromorphic vision systems for emerging AIOT applications.

March 23, 2023

Dr. Chang Gao has been selected for MIT Technology Review's prestigious Innovators Under 35 Europe list. The annual list recognizes outstanding young innovators from across the continent who have made significant contributions to technology and its potential to transform the world. Complete list of IU35 Europe: https://emtecheurope.com/innovators-2023.

February 13, 2023

I am happy to announce that our Marie Curie Skłodowska Fellowship project, “AIRHAR: An Energy-Efficient AI Powered Portable Radar System for Human Activity Recognition," in collaboration with Prof. Francesco Fioranelli, Prof. Alexander Yarovoy and Prof. Leo de Vreede, has been granted.

We are thrilled to explore the possibilities of leveraging AI model compression and edge AI hardware in radar sensing. We are eager to engage in fruitful conversations with industry partners on potential collaboration opportunities in this area.

January 15, 2023

We congratulate Dr. Chang Gao, leader of the EMI Lab, for winning the 2022 Mahowald Early Career Award for his project titled "Accelerating Recurrent Neural Networks with Neuromorphic Principles." The project introduces efficient computing using the neuromorphic principles of spatial and temporal sparsity, leading to an energy-efficient accelerator for edge RNN computing. Gao's accomplishments have the potential to revolutionize applications in healthcare, wearables, and intelligent biomedical implants.

Chang Gao, Tobi Delbruck, Shih-Chii Liu

IEEE Transactions on Neural Networks and Learning Systems (TNNLS)

[Journal Paper Link] [Preprint Link] [Code Link]

Kwantae Kim*, Chang Gao*, Rui Graça, Ilya Kiselev, Hoi-Jun Yoo, Tobi Delbruck, Shih-Chii Liu

2022 International Solid-State Circuits Conference (ISSCC) & IEEE Journal of Solid-State Circuits (JSSC)

[Conference Paper Link] [Journal Paper Link] [Preprint Link]