Zhiqiang Que
Lecturer (Assistant Professor) at University of Bristol
Stony Stratford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolZhiqiang Que is a Lecturer (Assistant Professor) at the University of Bristol with a decade of experience spanning academic research and industry engineering in ML/AI systems, hardware accelerators, and hardware–software co-design. He completed a PhD at Imperial College London and transitioned from hands-on CPU and FPGA design roles at Marvell, Xilinx, and China Financial Futures Exchange into research focused on efficient, trustworthy ML for real-time and edge inference. His work blends RTL-level expertise (CPU microarchitecture, dividers, load/store units) with algorithmic acceleration and design automation, giving him rare end-to-end visibility from silicon to models. At Imperial he led projects on high-performance FPGA computing and CT reconstruction, and he continues as a visiting scholar while lecturing, bridging cutting-edge research with teaching. Colleagues rely on him for pragmatic timing/power trade-offs and for translating academic advances into deployable hardware-aware AI systems.
10 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Imperial College LondonGraduate Computer System, Graduate Computer System at Shanghai Jiao Tong University
Github Skills (27)
lstm9
fpga9
resource-monitor7
deep-learning7
nvml7
grafana-dashboard6
process-monitoring6
nvidia-smi6
nvidia5
viewer5
console5
prometheus-exporter4
cuda4
curses4
command-line-tool4
Programming languages (4)
Github contributions (5)
walkieq/RNN_HLS
Mar 2021 - Aug 2021
An LSTM template and a few examples using Vivado HLS
Contributions:3 releases, 35 commits, 4 PRs in 4 months
deep-learningfpgalstm
walkieq/GNN-JEDInet-FPGA
Jun 2022 - Oct 2022
Contributions:10 commits, 8 pushes, 1 branch in 3 months
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