* Co-first Authors

Our MSc and PhD Students are highlighted in colors.

2025

• Y. Wu, F. Fioranelli, C. Gao, “RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space Model,” Preprint, Under Review (Code: https://github.com/lab-emi/AIRHAR)

• Y. Wu, Y. Zhu, K. Qian, Q. Chen, A. Zhu, J. Gajadharsing, L. C. N. de Vreede, C. Gao, “DeltaDPD: Exploiting Dynamic Temporal Sparsity in Recurrent Neural Networks for Energy-Efficient Wideband Digital Predistortion,” accepted to 2025 IEEE MTT-S International Microwave Symposium (IMS). (IMS 2025 Top 50 Paper, invited to IEEE MWTL)

• H. Duan, M. Versluis, Q. Chen, L. C. N. de Vreede, C. Gao, “TCN-DPD: Parameter-Efficient Temporal Convolutional Networks for Wideband Digital Predistortion,” accepted to 2025 IEEE MTT-S International Microwave Symposium (IMS).

• A. Li*, H. Wu*, Y. Wu, Q. Chen, L. C. N. de Vreede, C. Gao, “DPD-NeuralEngine: A 22-nm 6.6-TOPS/W/mm2 Recurrent Neural Network Accelerator for Wideband Power Amplifier Digital Pre-Distortion,” accepted to 2025 IEEE International Symposium on Circuits and Systems (ISCAS).

• S. Groot, Q. Chen, J. C. van Gemert, C. Gao, “CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel Pruning,” accepted to 2025 IEEE International Symposium on Circuits and Systems (ISCAS).

• Q. Chen*, K. Kim*, C. Gao*, S. Zhou, T. Jang, T. Delbruck, et al., "DeltaKWS: A 65nm 36nJ/Decision Bio-inspired Temporal-Sparsity-Aware Digital Keyword Spotting IC with 0.6V Near-Threshold SRAM," in IEEE Transactions on Circuits and Systems for Artificial Intelligence (TCASAI), 2025

• J. Ding, Z. Wang, C. Gao, M. Liu, Q. Chen, "FACET: Fast and Accurate Event-Based Eye Tracking Using Ellipse Modeling for Extended Reality," accepted to IEEE International Conference on Robotics and Automation (ICRA), 2025

• G. Lu, J. Peng, B. Huang, C. Gao, T. Stefanov, Y. Hao, Q. Chen, “SlimSeiz: Efficient Channel-Adaptive Seizure Prediction Using a Mamba-Enhanced Network,” accepted to 2025 IEEE International Symposium on Circuits and Systems (ISCAS).

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)

• S. -C. Liu, S. Zhou, Z. Li, C. Gao, K. Kim, and T. Delbruck, "Bringing Dynamic Sparsity to the Forefront for Low-Power Audio Edge Computing: Brain-inspired approach for sparsifying network updates," in IEEE Solid-State Circuits Magazine (SSC-M), vol. 16, no. 4, pp. 62-69, Fall 2024

• Z. Wang, C. Gao, Z. Wu, M. V. Conde, R. Timofte, S.-C. Liu, Q. Chen, et al., "Event-Based Eye Tracking. AIS 2024 Challenge Survey," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5810-5825

• 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 (Best Paper Award – Honorary Mention by the Neural Systems and Applications Technical Committee)

• 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 (JETCAS Spotlight Article 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

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• 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.