Design and Implementation of a Cordless Power Supply System for Pervasive Medical Devices. - Zhiqiang Que; Yongxin Zhu 001; Tingting Mo; Bin Chen; Zhijun Li (ICESS, 2009)

Implementing Medical CT Algorithms on Stand-alone FPGA Based Systems Using an Efficient Workflow with SysGen and Simulink. - Zhiqiang Que; Yongxin Zhu 001; Xuan Wang; Jibo Yu; Tian Huang; Zhe Zheng; Li Yang; Feng Zhao; Yuzhuo Fu (CIT, 2010)

Revealing Feasibility of FMM on ASIC: Efficient Implementation of N-Body Problem on FPGA. - Zhe Zheng; Yongxin Zhu 001; Xu Wang 010; Zhiqiang Que; Tian Huang; Xiaojing Yin; Hui Wang 036; Guoguang Rong; Meikang Qiu (CSE, 2010)

Reconfigurable Acceleration of 3D-CNNs for Human Action Recognition with Block Floating-Point Representation. - Hongxiang Fan; Ho-Cheung Ng; Shuanglong Liu; Zhiqiang Que; Xinyu Niu; Wayne Luk (FPL, 2018)

A Real-Time Object Detection Accelerator with Compressed SSDLite on FPGA. - Hongxiang Fan; Shuanglong Liu; Martin Ferianc; Ho-Cheung Ng; Zhiqiang Que; Shen Liu; Xinyu Niu; Wayne Luk (FPT, 2018)

Memory-Efficient Architecture for Accelerating Generative Networks on FPGA. - Shuanglong Liu; Chenglong Zeng; Hongxiang Fan; Ho-Cheung Ng; Jiuxi Meng; Zhiqiang Que; Xinyu Niu; Wayne Luk (FPT, 2018)

FPGA Acceleration of LSTM Based on Data for Test Flight. - Zhanrui Sun; Yongxin Zhu 001; Yu Zheng 012; Hao Wu; Zihao Cao; Peng Xiong; Junjie Hou; Tian Huang; Zhiqiang Que (SmartCloud, 2018)

Application of Transfer Learning in Continuous Time Series for Anomaly Detection in Commercial Aircraft Flight Data. - Peng Xiong; Yonxin Zhu; Zhanrui Sun; Zihao Cao; Menglin Wang; Yu Zheng 012; Junjie Hou; Tian Huang; Zhiqiang Que (SmartCloud, 2018)

Reconfigurable Hardware Generation for Tensor Flow Models of CNN Algorithms on a Heterogeneous Acceleration Platform. - Jiajun Gao; Yongxin Zhu 001; Meikang Qiu; Kuen Hung Tsoi; Xinyu Niu; Wayne Luk; Ruizhe Zhao; Zhiqiang Que; Wei Mao; Can Feng; Xiaowen Zha; Guobao Deng; et al. (SmartCom, 2018)

F-E3D: FPGA-based Acceleration of an Efficient 3D Convolutional Neural Network for Human Action Recognition. - Hongxiang Fan; Cheng Luo; Chenglong Zeng; Martin Ferianc; Zhiqiang Que; Shuanglong Liu; Xinyu Niu; Wayne Luk (ASAP, 2019)

Efficient Weight Reuse for Large LSTMs. - Zhiqiang Que; Thomas Nugent; Shuanglong Liu; Li Tian; Xinyu Niu; Yongxin Zhu 001; Wayne Luk (ASAP, 2019)

Towards In-Circuit Tuning of Deep Learning Designs. - Zhiqiang Que; Daniel Holanda Noronha; Ruizhe Zhao; Steven J. E. Wilton; Wayne Luk (ICCAD, 2019)

An Overlay for Rapid FPGA Debug of Machine Learning Applications. - Daniel Holanda Noronha; Ruizhe Zhao; Zhiqiang Que; Jeffrey Goeders; Wayne Luk; Steven J. E. Wilton (FPT, 2019)

Real-Time Anomaly Detection for Flight Testing Using AutoEncoder and LSTM. - Zhiqiang Que; Yanyang Liu; Ce Guo; Xinyu Niu; Yongxin Zhu 001; Wayne Luk (FPT, 2019)

An efficient convolutional neural network for small traffic sign detection. - Shijin Song; Zhiqiang Que; Junjie Hou; Sen Du; Yuefeng Song (J. Syst. Archit., 2019)

High-Throughput Convolutional Neural Network on an FPGA by Customized JPEG Compression. - Hiroki Nakahara; Zhiqiang Que; Wayne Luk (FCCM, 2020)

Optimizing Reconfigurable Recurrent Neural Networks. - Zhiqiang Que; Hiroki Nakahara; Eriko Nurvitadhi; Hongxiang Fan; Chenglong Zeng; Jiuxi Meng; Xinyu Niu; Wayne Luk (FCCM, 2020)

R2CNN: Recurrent Residual Convolutional Neural Network on FPGA. - Hiroki Nakahara; Zhiqiang Que; Akira Jinguji; Wayne Luk (FPGA, 2020)

Optimizing FPGA-Based CNN Accelerator Using Differentiable Neural Architecture Search. - Hongxiang Fan; Martin Ferianc; Shuanglong Liu; Zhiqiang Que; Xinyu Niu; Wayne Luk (ICCD, 2020)

A Reconfigurable Multithreaded Accelerator for Recurrent Neural Networks. - Zhiqiang Que; Hiroki Nakahara; Hongxiang Fan; Jiuxi Meng; Kuen Hung Tsoi; Xinyu Niu; Eriko Nurvitadhi; Wayne Luk (FPT, 2020)

Towards Overlay-based Rapid In-Circuit Tuning of Deep Learning Designs. - Zhiqiang Que; Daniel Holanda Noronha; Ruizhe Zhao; Xinyu Niu; Steven J. E. Wilton; Wayne Luk (FPT, 2020)

Mapping Large LSTMs to FPGAs with Weight Reuse. - Zhiqiang Que; Yongxin Zhu 001; Hongxiang Fan; Jiuxi Meng; Xinyu Niu; Wayne Luk (J. Signal Process. Syst., 2020)

Accelerating Recurrent Neural Networks for Gravitational Wave Experiments. - Zhiqiang Que; Erwei Wang; Umar Marikar; Eric A. Moreno; Jennifer Ngadiuba; Hamza Javed; Bartlomiej Borzyszkowski; Thea Aarrestad; Vladimir Loncar; Sioni Summers; Maurizio Pierini; Peter Y. K. Cheung; et al. (ASAP, 2021)

Flexible Instrumentation for Live On-Chip Debug of Machine Learning Training on FPGAs. - Daniel Holanda Noronha; Zhiqiang Que; Wayne Luk; Steven J. E. Wilton (FCCM, 2021)

Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator. - Martin Ferianc; Zhiqiang Que; Hongxiang Fan; Wayne Luk; Miguel Rodrigues 001 (FPT, 2021)

High-Performance FPGA-based Accelerator for Bayesian Recurrent Neural Networks. - Martin Ferianc; Zhiqiang Que; Hongxiang Fan; Wayne Luk; Miguel Rodrigues 001 (CoRR, 2021)

Applications and Techniques for Fast Machine Learning in Science. - Allison McCarn Deiana; Nhan Tran; Joshua Agar; Michaela Blott; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Scott Hauck; Mia Liu; Mark S. Neubauer; Jennifer Ngadiuba; Seda Ogrenci Memik; et al. (CoRR, 2021)

In-circuit tuning of deep learning designs. - Zhiqiang Que; Daniel Holanda Noronha; Ruizhe Zhao; Xinyu Niu; Steven J. E. Wilton; Wayne Luk (J. Syst. Archit., 2021)

Reconfigurable Acceleration of Graph Neural Networks for Jet Identification in Particle Physics. - Zhiqiang Que; Marcus Loo; Wayne Luk (AICAS, 2022)

Hardware-Aware Optimizations for Deep Learning Inference on Edge Devices. - Markus Rognlien; Zhiqiang Que; José Gabriel F. Coutinho; Wayne Luk (ARC, 2022)

Algorithm and Hardware Co-design for Reconfigurable CNN Accelerator. - Hongxiang Fan; Martin Ferianc; Zhiqiang Que; He Li 008; Shuanglong Liu; Xinyu Niu; Wayne Luk (ASP-DAC, 2022)

Optimizing Graph Neural Networks for Jet Tagging in Particle Physics on FPGAs. - Zhiqiang Que; Marcus Loo; Hongxiang Fan; Maurizio Pierini; Alexander D. Tapper; Wayne Luk (FPL, 2022)

Accelerating Transformer Neural Networks on FPGAs for High Energy Physics Experiments. - Filip Wojcicki; Zhiqiang Que; Alexander D. Tapper; Wayne Luk (FPT, 2022)

Verifying Hardware Optimizations for Efficient Acceleration. - Qianzhou Wang; Yat Wong; Zhiqiang Que; Wayne Luk (HEART, 2022)

Customizable FPGA-based Accelerator for Binarized Graph Neural Networks. - Ziwei Wang; Zhiqiang Que; Wayne Luk; Hongxiang Fan (ISCAS, 2022)

LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors. - Zhiqiang Que; Marcus Loo; Hongxiang Fan; Michaela Blott; Maurizio Pierini; Alexander D. Tapper; Wayne Luk (CoRR, 2022)

FPGA-Based Acceleration for Bayesian Convolutional Neural Networks. - Hongxiang Fan; Martin Ferianc; Zhiqiang Que; Shuanglong Liu; Xinyu Niu; Miguel R. D. Rodrigues; Wayne Luk (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022)

Accelerating Bayesian Neural Networks via Algorithmic and Hardware Optimizations. - Hongxiang Fan; Martin Ferianc; Zhiqiang Que; Xinyu Niu; Miguel R. D. Rodrigues; Wayne Luk (IEEE Trans. Parallel Distributed Syst., 2022)

Recurrent Neural Networks With Column-Wise Matrix-Vector Multiplication on FPGAs. - Zhiqiang Que; Hiroki Nakahara; Eriko Nurvitadhi; Andrew Boutros; Hongxiang Fan; Chenglong Zeng; Jiuxi Meng; Kuen Hung Tsoi; Xinyu Niu; Wayne Luk (IEEE Trans. Very Large Scale Integr. Syst., 2022)

When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA. - Hongxiang Fan; Mark Chen; Liam Castelli; Zhiqiang Que; He Li 008; Kenneth Long; Wayne Luk (DAC, 2023)

MetaML: Automating Customizable Cross-Stage Design-Flow for Deep Learning Acceleration. - Zhiqiang Que; Shuo Liu; Markus Rognlien; Ce Guo; José Gabriel F. Coutinho; Wayne Luk (FPL, 2023)

Efficiently Removing Sparsity for High-Throughput Stream Processing. - Philippos Papaphilippou; Zhiqiang Que; Wayne Luk (ICFPT, 2023)

When Monte-Carlo Dropout Meets Multi-Exit: Optimizing Bayesian Neural Networks on FPGA. - Hongxiang Fan; Hao Chen; Liam Castelli; Zhiqiang Que; He Li 008; Kenneth Long; Wayne Luk (CoRR, 2023)

Remarn: A Reconfigurable Multi-threaded Multi-core Accelerator for Recurrent Neural Networks. - Zhiqiang Que; Hiroki Nakahara; Hongxiang Fan; He Li 008; Jiuxi Meng; Kuen Hung Tsoi; Xinyu Niu; Eriko Nurvitadhi; Wayne Luk (ACM Trans. Reconfigurable Technol. Syst., 2023)

High-Performance Acceleration of 2-D and 3-D CNNs on FPGAs Using Static Block Floating Point. - Hongxiang Fan; Shuanglong Liu; Zhiqiang Que; Xinyu Niu; Wayne Luk (IEEE Trans. Neural Networks Learn. Syst., 2023)

Optimizing DNN Accelerator Compression Using Tolerable Accuracy Loss. - Zhiqiang Que; Anyan Zhao; José Gabriel F. Coutinho; Ce Guo; Wayne Luk (ICFPT, 2024)

Trustworthy Codesign by Verifiable Transformations. - Qianzhou Wang; Zhiqiang Que; Wayne Luk (ITC-Asia, 2024)

Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC. - Patrick Odagiu; Zhiqiang Que; Javier M. Duarte; Johannes Haller; Gregor Kasieczka; Artur Lobanov; Vladimir Loncar; Wayne Luk; Jennifer Ngadiuba; Maurizio Pierini; Philipp Rincke; Arpita Seksaria; et al. (CoRR, 2024)

Ultrafast jet classification at the HL-LHC. - Patrick Odagiu; Zhiqiang Que; Javier M. Duarte; Johannes Haller; Gregor Kasieczka; Artur Lobanov; Vladimir Loncar; Wayne Luk; Jennifer Ngadiuba; Maurizio Pierini; Philipp Rincke; Arpita Seksaria; et al. (Mach. Learn. Sci. Technol., 2024)

LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics. - Zhiqiang Que; Hongxiang Fan; Marcus Loo; He Li 008; Michaela Blott; Maurizio Pierini; Alexander D. Tapper; Wayne Luk (ACM Trans. Embed. Comput. Syst., 2024)

Low Latency Variational Autoencoder on FPGAs. - Zhiqiang Que; Minghao Zhang; Hongxiang Fan; He Li 008; Ce Guo; Wayne Luk (IEEE J. Emerg. Sel. Topics Circuits Syst., 2024)

Trustworthy Deep Learning Acceleration with Customizable Design Flow Automation. - Zhiqiang Que; Hongxiang Fan; Gabriel F. Figueiredo; Ce Guo; Wayne Luk; Ryota Yasudo; Masato Motomura (HEART, 2025)

MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep Learning Acceleration. - Zhiqiang Que; José Gabriel F. Coutinho; Ce Guo; Hongxiang Fan; Wayne Luk (CoRR, 2025)

da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs. - Chang Sun; Zhiqiang Que; Vladimir Loncar; Wayne Luk; Maria Spiropulu (CoRR, 2025)