* 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.