The EMI Lab - TU Delft

The Efficient circuits & systems for Machine Intelligence (EMI) research lab focuses on designing energy-efficient digital AI hardware for edge computing, emphasizing ultrahigh-speed communication, video/audio processing, robotics, and biomedical applications. We aim to bridge the gap between artificial neural networks (ANNs) and spiking neural networks (SNNs) by applying brain-inspired neuromorphic principles to massively accelerate the computation of state-of-the-art deep neural network (DNN) architectures while maintaining competitive accuracy on real-world tasks (We are hiring!).

Dr. Chang Gao - Lab PI

Tenure-Track Assistant Professor

Electronic Circuits and Architecture (ELCA) Group

Department of Microelectronics

Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS)

Delft University of Technology (TU Delft)

Delft, Netherlands

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130K Swiss Francs Fund Received by Our Partner

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.

New TVLSI Paper on Accelerating Spiking Neural Networks

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.

Research Highlights

Robotic Prosthesis Control

Collaboration with the AMBER Lab, Caltech

Spoken Digit Recogntion

Using the EdgeDRNN Accelerator

Neuromorphic Computing

Interfacing EdgeDRNN with a Silicon Cochlea

Recent Publications

Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity (2022)

A 23μW Solar-Powered Keyword-Spotting ASIC with Ring-Oscillator-Based Time-Domain Feature Extraction (2022)

How to Find Us