Chang Gao's Research Lab

Publications

Please track our latest publications on Chang Gao's Google Scholar page.

* Co-first Authors/Corresponding Authors

2024

Y. Wu, G. Singh, M. Beikmirza, L. de Vreede, M. Alavi, C. Gao*, "OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion," accepted to 2024 IEEE International Symposium on Circuits and Systems (ISCAS), 2024 (Invited to the RFIC & AI Special Session, Code: https://github.com/lab-emi/OpenDPD)
Y. Wu, A. Li, M. Beikmirza, G. Singh, Q. Chen, L. de Vreede, M. Alavi, C. Gao*, "MP-DPD: Low-Complexity Mixed-Precision Neural Networks for Energy-Efficient Digital Pre-distortion of Wideband Power Amplifiers," accepted to 2024 IEEE International Microwave Symposium (IMS), 2024 (Ranked in Top 50, Code: https://github.com/lab-emi/OpenDPD)
Q. Chen, C. Sun, C. Gao, S.-C. Liu, "Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer," accepted to 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 2024
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), 2024
X. Chen, C. Gao, Z. Wang, L. Cheng, S. Zhou, S.-C. Liu and T. Delbruck, "Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training," accepted to the 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024
S. Li et al., "HAS-RL: A Hierarchical Approximate Scheme Optimized With Reinforcement Learning for NoC-Based NN Accelerators," in IEEE Transactions on Circuits and Systems I: Regular Papers (TCAS-I), 2024

2023

Q. Chen, Z. Wang, S.-C. Liu, C. Gao*, "3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network," in 2023 IEEE Biomedical Circuits and Systems (BioCAS) Conference, 2023
F, Ottati, C. Gao, Q. Chen, G. Brignone, M. R. Casu, J. K. Eshraghian, L. Lavagno, "To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration," in IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2023
Q. Chen, Y. Chang, K. Kim, C. Gao*, S.-C. Liu*, "An Area-Efficient Ultra-Low-Power Time-Domain Feature Extractor for Edge Keyword Spotting," in 2023 IEEE International Symposium on Circuits and Systems (ISCAS), 2023
Q. Chen, C. Sun, C. Gao, X. Fang and H. Luan, "FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation," in 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2023

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
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.