dr. C Gao
Electronic Circuits and Architectures (ELCA), Department of Microelectronics
Promotor:
Expertise: Digital Circuit Design, AI Hardware, Neuromorphic Computing, Artificial Intelligence
Themes: Health and Wellbeing, XG - Next Generation Sensing and CommunicationBiography
Dr. Chang Gao is an assistant professor at the Department of Microelectronics, TU Delft, where he leads the Lab of Efficient circuits & systems for Machine Intelligence (EMI).
His research focuses on designing energy-efficient digital AI hardware for edge computing, emphasizing ultrahigh-speed communication, video/audio processing, robotics, and biomedical applications. He applies brain-inspired neuromorphic principles to bridge the gap between artificial neural networks (ANNs) and spiking neural networks (SNNs), achieving massive acceleration and competitive accuracy on real-world tasks. He has published multiple papers in prestigious journals and conferences and received the Best Paper Award at the 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). He holds a PhD with Distinction from the University of Zurich and ETH Zurich and an MSc in Analogue and Digital Integrated Circuit Design from Imperial College London.
He is a co-recipient of the 2020 Misha Mahowald Prize for Neuromorphic Engineering and the 2022 Mahowald Early Career Award in Neuromorphic Engineering. He recently secured the 2022 Marie Skłodowska-Curie Postdoctoral Fellowship and was named a 2023 MIT Technology Review Innovator Under 35.
Links:
Last updated: 4 Sep 2023

Chang Gao
- Chang.Gao@tudelft.nl
- Room: HB 19.280
- Personal webpage
- Google Scholar profile
- Download CV
MSc students
MSc project proposals
- [2023] Small-footprint Embedded Real-Time Speech Enhancement for Cochlea Implant
- [2023] Hardware Accelerated AI for Digital Predistortion
- [2023] Efficient Transformer for Keyword Spotting
- [2023] DL4RF: Deep Learning for Wireless Transmitter Efficiency Enhancement
- [2023] Build your own Mini-ChatGPT on Jetson Nano
- [2023] Neuromorphic Real-Time Audio Denoiser