Publications
Please track our latest publications on Chang Gao's Google Scholar page.
* Co-first Authors
2022
S. -C. Liu, C. Gao, K. Kim, T. Delbruck, "Energy-Efficient Activity-Driven Computing Architectures for Edge Intelligence," in 2022 IEEE International Electron Devices Meeting (IEDM), 2022
C. Gao, T. Delbruck and S. -C. Liu, "Spartus: A 9.4 TOp/s FPGA-Based LSTM Accelerator Exploiting Spatio-Temporal Sparsity," in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022.
K. Kim*, C. Gao*, et al., "A 23μW Solar-Powered Keyword-Spotting ASIC with Ring-Oscillator-Based Time-Domain Feature Extraction," 2022 IEEE International Solid-State Circuits Conference (ISSCC), 2022, pp. 1-3.
K. Kim, C. Gao, R. Graça, I. Kiselev, H.-J. Yoo, T. Delbruck and S. -C. Liu, "A 23 μW Keyword Spotting IC with Ring-Oscillator-Based Time-Domain Feature Extraction," in IEEE Journal of Solid-State Circuits (JSSC), 2022.
Q. Chen*, C. Gao* and Y. Fu, "Cerebron: A Reconfigurable Architecture for Spatio-Temporal Sparse Spiking Neural Networks," in IEEE Transactions on Very Large Scale Integration Systems (TVLSI), 2022.
Q. Chen*, C. Gao*, X. Fang and H. Luan, "Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD).
I. Kiselev, C. Gao and S. -C. Liu, "Spiking Cochlea with System-Level Local Automatic Gain Control," in IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), vol. 69, no. 5, pp. 2156-2166, May 2022.
Q. Chen, C. Sun, Z. Lu and C. Gao, "Enabling Energy-Efficient Inference for Self-Attention Mechanisms in Neural Networks," 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2022, pp. 25-28
J. H. Lindmar, C. Gao and S. -C. Liu, "Intrinsic Sparse LSTM using Structured Targeted Dropout for Efficient Hardware Inference," 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2022, pp. 126-129
2021
X. Chen, C. Gao, T. Delbruck and S. -C. Liu, "EILE: Efficient Incremental Learning on the Edge," 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2021, pp. 1-4.
2020
C. Gao, A. Rios-Navarro, X. Chen, S. -C. Liu and T. Delbruck, "EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference," in IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), vol. 10, no. 4, pp. 419-432, Dec. 2020.
C. Gao*, R. Gehlhar*, A. D. Ames, S. -C. Liu and T. Delbruck, "Recurrent Neural Network Control of a Hybrid Dynamical Transfemoral Prosthesis with EdgeDRNN Accelerator," 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 5460-5466.
C. Gao, A. Rios-Navarro, X. Chen, T. Delbruck and S. -C. Liu, "EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference," 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2020, pp. 41-45. (Best Paper Award)
2019
C. Gao, S. Braun, I. Kiselev, J. Anumula, T. Delbruck and S. Liu, "Real-Time Speech Recognition for IoT Purpose using a Delta Recurrent Neural Network Accelerator," 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019, pp. 1-5.
C. Gao, S. Braun, I. Kiselev, J. Anumula, T. Delbruck and S. Liu, "Live Demonstration: Real-Time Spoken Digit Recognition using the DeltaRNN Accelerator," 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019, pp. 1-1.
2018
C. Gao, D. Neil, E. Ceolini, S.-C. Liu, and T. Delbruck, "DeltaRNN: A Power-efficient Recurrent Neural Network Accelerator," In Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA '18). Association for Computing Machinery, New York, NY, USA, 21–30.
2017
C. Gao, S. Ghoreishizadeh, Y. Liu and T. Constandinou, "On-chip ID generation for multi-node implantable devices using SA-PUF," 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017, pp. 1-4.
Awards
The Mahowald Early Career Award (MECA) recognizes an exceptionally talented student, as Misha Mahowald was. The Award will be made for an innovative project that addresses a significant problem in the domain of neuromorphic engineering or related problems in neuroscience and neural computation. In their submission, candidates must make a convincing case for why their project fulfills this criterion.
Distinction of PhD Thesis (Chang Gao, 2022)
2,000 CHF awarded by the Canton of Zurich & the Faculty of Science, University of Zurich
IEEE AICAS Best Paper Award (Chang Gao, 2020)
Overall best paper award at the 2020 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
As a co-recipient in the Dynamic Audio Sensor development team led by Prof. Shih-Chii Liu
Outstanding Self-financed Students Abroad (Chang Gao, 2020)
6,000 USD awarded by the China Scholarship Council (CSC)
GRC Travel Grant (Chang Gao, 2019)
1,900 CHF travel grant for the 2019 Telluride Neuromorphic Workshop awarded by the Graduate Campus (GRC), University of Zurich
MSc Outstanding Achievement (Chang Gao, 2016)
Top 1 student in the MSc Analog & Digital Integrated Circuit Design program, Imperial College London