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.

August 29, 2022

We congratulate Dr. Qinyu Chen again for winning the BRIDGE Proof of Concept grant for her postdoctoral project. The 130,000 CHF fund will be released by the Swiss National Science Foundation (SNSF) and Innosuisse - Swiss Innovation Agency. The EMI lab will collaborate with Dr. Qinyu Chen, a Postdoctoral Researcher in the Sensors Group at the Institute of Neuroinformatics, University of Zurich and ETH Zurich to build next-generation edge AI chips for healthcare.

August 17, 2022

We congratulate Dr. Qinyu Chen and other co-authors for publishing the paper "Cerebron: A Reconfigurable Architecture for Spatio-Temporal Sparse Spiking Neural Networks" in the IEEE Transactions on Very Large Scale Integration Systems (TVLSI). See our paper at this link.