Zhiqiang (Walkie) Que

Please see my DBLP list or Google Scholar for details.


[J4] Zqiang Que, Hiroki Nakahara, Eriko Nurvitadhi, Andrew Boutros, Hongxiang Fan, Chenglong Zeng, Jiuxi Meng, Kuen Hung Tsoi, Xinyu Niu, Wayne Luk. Recurrent Neural Networks With Column-Wise Matrix-Vector Multiplication on FPGAs, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2021(Early Access)
[Link] [PDF]


[J3] Zhiqiang Que, Daniel Holanda Noronha, Ruizhe Zhao, Xinyu Niu, Steven JE Wilton, Wayne Luk. In-circuit tuning of deep learning designs, Journal of System Architecture, 2021
[Link] [PDF]

[C23] Martin Ferianc, Zhiqiang Que, Hongxiang Fan, Wayne Luk, and Miguel Rodrigues Optimizing Bayesian Recurrent Neural Networks on an FPGA-based Accelerator Will appear in the International Conference on Field-Programmable Technologies (FPT), 2021.

[C22] Zhiqiang Que, Erwei Wang, Umar Marikar, Eric Moreno, Jennifer Ngadiuba, Hamza Javed, Bartłomiej Borzyszkowski, Thea Aarrestad, Vladimir Loncar, Sioni Summers, Maurizio Pierini, Peter Y Cheung, Wayne Luk.
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments, ASAP 2021
[Link] [PDF] [Github]

[C21] Daniel Holanda Noronha, Zhiqiang Que, Wayne Luk, Steven JE Wilton. Flexible Instrumentation for Live On-Chip Debug of Machine Learning Training on FPGAs, FCCM 2021
[Link] [PDF]


[J2] Z. Que, Y. Zhu, H. Fan, J. Meng, X. Niu, W. Luk. Mapping Large LSTMs to FPGAs with Weight Reuse, Journal of Signal Processing Systems, 2020
[Link] [PDF]

[C20] Z. Que, H. Nakahara, H. Fan, J. Meng, K. H.Tsoi, X. Niu, E. Nurvitadhi, W. Luk. A Reconfigurable Multithreaded Accelerator for Recurrent Neural Networks, FPT 2020 (25% acceptance rate)
[VIDEO] [Link] [PDF]

[C19] Z. Que, D. Noronha, R. Zhao, X. Niu, S. Wilton, W. Luk Towards Overlay-based Rapid In-Circuit Tuning of Deep Learning Designs, poster FPT 2020

[C18] H. Fan, M. Ferianc, S. Liu, Z. Que, X. Niu, W. Luk. Optimizing FPGA-Based CNN Accelerator Using Differentiable Neural Architecture Search, ICCD 2020

[C17] H. Nakahara, Z. Que, W. Luk. High-Throughput Convolutional Neural Network on an FPGA by Customized JPEG Compression, FCCM 2020 (Best paper nomination)
[Link] [PDF]

[C16] Z. Que, H. Nakahara, E. Nurvitadhi, H. Fan, C. Zeng, J. Meng, X. Niu, W. Luk. Optimizing Reconfigurable Recurrent Neural Networks, FCCM 2020, (20.6% acceptance rate)
[Link] [PDF]

[C15] H. Nakahara, Z. Que, A. Jinguji, and W. Luk. R2CNN: Recurrent Residual Convolutional Neural Network on FPGA (Abstract), In The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA), 2020


[J1] S. Song, Z. Que, J. Hou, S. Du, and Y. Song. An efficient convolutional neural network for small traffic sign detection, Journal of Systems Architecture, 97, 2019

[C14] H. Fan, C. Luo, C. Zeng, M. Ferianc, Z. Que, S. Liu, X. Niu, W. Luk. [ F-E3D: FPGA-based Acceleration of an Efficient 3D Convolutional Neural Network for Human Action Recognition, In 2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2019 (Best paper nomination)
[Link] [PDF]

[C13] Zhiqiang Que, Thomas Nugent, Shuanglong Liu, Li Tian, Xinyu Niu, Yongxin Zhu, Wayne Luk. Efficient Weight Reuse for Large LSTMs, ASAP 2019: 17-24
[Link] [PDF]

[C12] Zhiqiang Que, Daniel Holanda Noronha, Ruizhe Zhao, Steven J. E. Wilton, Wayne Luk. Towards In-Circuit Tuning of Deep Learning Designs, ICCAD 2019: 1-6
[Link] [PDF]

[C11] Daniel Holanda Noronha, Ruizhe Zhao, Zhiqiang Que, Jeffrey Goeders, Wayne Luk, Steven J. E. Wilton. An Overlay for Rapid FPGA Debug of Machine Learning Applications, FPT 2019: 135-143 (Best paper nomination)
[Link] [PDF]

[C10] Zhiqiang Que, Yanyang Liu, Ce Guo, Xinyu Niu, Yongxin Zhu, Wayne Luk. Real-time Anomaly Detection for Flight Testing using AutoEncoder and LSTM, FPT 2019: 379-382
[Link] [PDF]


[C9] Hongxiang Fan, Ho-Cheung Ng, Shuanglong Liu, Zhiqiang Que, Xinyu Niu, Wayne Luk. Reconfigurable Acceleration of 3D-CNNs for Human Action Recognition with Block Floating-Point Representation. FPL 2018: 287-294

[C8] Hongxiang Fan, Shuanglong Liu, Martin Ferianc, Ho-Cheung Ng, Zhiqiang Que, Shen Liu, Xinyu Niu, Wayne Luk. A Real-Time Object Detection Accelerator with Compressed SSDLite on FPGA. FPT 2018: 14-21 (Best paper nomination)

[C7] Shuanglong Liu, Chenglong Zeng, Hongxiang Fan, Ho-Cheung Ng, Jiuxi Meng, Zhiqiang Que, Xinyu Niu, Wayne Luk. Memory-Efficient Architecture for Accelerating Generative Networks on FPGA. FPT 2018: 30-37

[C6] Zhanrui Sun, Yongxin Zhu, Yu Zheng, Hao Wu, Zihao Cao, Peng Xiong, Junjie Hou, Tian Huang, Zhiqiang Que: FPGA Acceleration of LSTM Based on Data for Test Flight. SmartCloud 2018: 1-6 (Best paper)

[C5] Peng Xiong, Yonxin Zhu, Zhanrui Sun, Zihao Cao, Menglin Wang, Yu Zheng, Junjie Hou, Tian Huang, Zhiqiang Que. Application of Transfer Learning in Continuous Time Series for Anomaly Detection in Commercial Aircraft Flight Data. SmartCloud 2018: 13-18

[C4] Jiajun Gao, Yongxin Zhu, Meikang Qiu, Kuen Hung Tsoi, Xinyu Niu, Wayne Luk, Ruizhe Zhao, Zhiqiang Que, Wei Mao, Can Feng, Xiaowen Zha, Guobao Deng, Jiayi Chen, Tao Liu. Reconfigurable Hardware Generation for Tensor Flow Models of CNN Algorithms on a Heterogeneous Acceleration Platform. SmartCom 2018: 87-96

Before 2011

[C3] Zhiqiang Que, Yongxin Zhu, Xuan Wang, Jibo Yu, Tian Huang, Zhe Zheng, Li Yang, Feng Zhao, Yuzhuo Fu. Implementing Medical CT Algorithms on Stand-alone FPGA Based Systems Using an Efficient Workflow with SysGen and Simulink. CIT 2010: 2391-2396

[C2] Zhe Zheng, Yongxin Zhu, Xu Wang, Zhiqiang Que, Tian Huang, Xiaojing Yin, Hui Wang, Guoguang Rong, Meikang Qiu. Revealing Feasibility of FMM on ASIC: Efficient Implementation of N-Body Problem on FPGA. CSE 2010: 132-139 (Best paper)

[C1] Zhiqiang Que, Yongxin Zhu, Tingting Mo, Bin Chen, Zhijun Li. Design and Implementation of a Cordless Power Supply System for Pervasive Medical Devices. ICESS 2009: 547-552