Publications.

Y. Wu, A. Li, C. Gao, “OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion,” Preprint, accepted to the 2026 IEEE Global Communications Conference (GLOBECOM), 2026 (Code: https://github.com/lab-emi/OpenDPD)

A. Li and C. Gao, "BitFair: A 12nm Bit-Serial CNN Accelerator with Learnable Early Termination and Adaptive Bit Ordering for Ultra-Low-Power XR Vision," in IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS), 2026

Y. Zhu, F. V. Rijs, L. C. N. d. Vreede, J. Gajadharsing and C. Gao, "Deep-Learning Based Hardware-Efficient Load Identification for Power Amplifiers," in IEEE Journal of Selected Topics in Electromagnetics, Antennas and Propagation (JSTEAP), 2026.

T. Han, A. Li, Q. Chen and C. Gao, "JaneEye: A 12-nm 2K-FPS 18.9-μJ/Frame Event-based Eye Tracking Accelerator," 2026 31st Asia and South Pacific Design Automation Conference (ASP-DAC), Lantau, Hong Kong, 2026, pp. 170-176, 2026

Earlier acceptance announcement

T. Han, A. Li, Q. Chen, C. Gao, “JaneEye: A 12-nm 2K-FPS 18.9-μJ/Frame Event-based Eye Tracking Accelerator,” accepted to 30th Asia and South Pacific Design Automation Conference (ASP-DAC), Hong Kong, China, 2026

A. de Beer and C. Gao, "SlimTTS: Parameter- and Compute-Efficient Neural Text-to-Speech Synthesis for Real-Time Inference," 2026 IEEE International Symposium on Circuits and Systems (ISCAS), Shanghai, China, 2026.

Y. Wu, F. Fioranelli, C. Gao, “RadMamba: Efficient Human Activity Recognition through Radar-based Micro-Doppler-Oriented Mamba State-Space Model,” in IEEE Transactions on Radar Systems (T-RS), vol. 4, pp. 261-272, 2026 (Code: https://github.com/lab-emi/AIRHAR)

• Fioranelli

C. Gao, S.-C. Liu, T. Delbruck, and X. Chen, “Neural network-based inference method and apparatus,” U.S. Patent 12,299,576, May 13, 2025.

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,” in 2025 IEEE MTT-S International Microwave Symposium (IMS). (IMS 2025 Top 50 Paper, invited to IEEE MWTL)

S. Zhou, C. Gao, T. Delbruck, M. Verhelst, S.-C. Liu, "Exploiting neuro-inspired dynamic sparsity for energy-efficient intelligent perception," in Nature Communications, 2025

M. Versluis, Y. Wu, C. Gao, “SparseDPD: A Sparse Neural Network-based Digital Predistortion FPGA Accelerator for RF Power Amplifier Linearization,” accepted to 35th International Conference on Field-Programmable Logic and Applications (FPL), Leiden, The Netherlands, 2025.

C. Bao, C. Huo, Q. Chen, C. Gao, “AS-ASR: A Lightweight Framework for Aphasia-Specific Automatic Speech Recognition,” accepted to 2025 IEEE Biomedical Circuits and Systems (BioCAS) Conference, Abu Dhabi, United Arab Emirates, 2025

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), 2025.

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).

Y. Zhu, M. Schmitdt-Szalowski, P. Hammes, R. Ouhachi, V. Cuoco, C. Gao, Q. Tao, J. Gajadharsing, "Automatic I–V Parameter Extraction for GaN Devices With Image-Based Machine Learning Method," in IEEE Microwave and Wireless Technology Letters (MWTL)

S. Zhou, Z. Li, L. Cheng, J. Hadorn, C. Gao, Q. Chen, T. Delbruck, K. Kim, S.-C. Liu, "An 8.62-μW 75-dB DRSoC Fully Integrated SoC for Spoken Language Understanding," in IEEE Journal of Solid-State Circuits (JSSC)

J. Qi, C. Gao, Z. Ren, Q. Chen, “DeltaLLM: A Training-Free Framework Exploiting Temporal Sparsity for Efficient Edge LLM Inference,” preprint, 2025.

Z. Xu, G. Lu, C. Gao, Q. Chen, “EvHand-FPV: Efficient Event-Based 3D Hand Tracking from First-Person View,” preprint, 2025.

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).

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

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)

• 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

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

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.

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)

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.

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.

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.