Hongxiang Fan

Ph.D. Student at Imperial College London

High Performance Computing and Machine Learning

Download Resume

I am currently a second-year Ph.D. student in Machine Learning and High-Performance Computing at Imperial College London, supervised by Prof. Wayne Luk.

My current research focuses on efficient algorithm and acceleration for Machine Learning applications. My previous research is about Quantization (Block Floating Point and Partial Quantization) and efficient structure (F-E3D) on various hardware platforms. I also have experience with machine learning on the mobile platform (Android Demo).


Imperial College London

October 2018 – Present

Doctor of Philosophy (Ph.D.) in Advanced Computing

Imperial College London

Sept 2017 - Sept 2018

Master of Research (MRes) in Computing

Nanyang Technological University

July 2015 - Jan 2016

Exchange Student in Electrical and Electronic Engineering

Tianjin University

Sept 2013 - June 2017

Bachelor of Engineering (B.Eng.) in Electrical and Electronic Engineering


Selected Publications

F-E3D: FPGA-based Acceleration of An Efficient 3D Convo-lutional Neural Networkfor Human Action Recognition

2019 International Conference on Application-specific Systems,
Architectures and Processors (ASAP)
Fan, Hongxiang and W. Luk
Best Paper Nomination

[PDF] [Code]

A Real-Time Object Detection Acceleratorwith Compressed SSDLite on FPGA

2018 International Conference on Field Programmable Technology (ICFPT)
Fan, Hongxiang and W. Luk
Best Paper Nomination


Reconfigurable Acceleration of 3D-CNNs for HumanAction Recognition with Block Floating-Point Representation

2018 International Conferenceon Field Programmable Logic and Applications (FPL)
Fan, Hongxiang, H.-C. Ng, and W. Luk,


F-C3D: FPGA-based 3-dimensional Convolutional Neural Network

2017 International Conference on Field Programmable Logic and Applications (FPL)
Fan, Hongxiang, X. Niu, Q. Liu, and W. Luk,



Tianjin University

Research Assistant

I was a research assistant with Prof. Qiang Liu at Tianjin University. The project (publication) is about FPGA-based hardware emulation for IC security evaluation. My responsibility was building a software tool using Python to automatically extract the necessary information from the synthesized netlist.

Imperial College London

Research Internship in Department of Computing

Under the supervision of Prof. Wayne Luk, the research studied the FPGA-based acceleration of 3D CNNs,which led to the publication in FPL’17. The internship was sponsored by Tianjin University with full scholarship.

Corerain Technologies

AI Researcher

My work focused on the optimization and acceleration of deep learning algorithm. Based on my proposed partial quantization technique, the processing speed of the face detection product increased from 30 framesper second (fps) to 100 fps with negligible accuracy loss. I also helped in designing the hardware architecture for deep learning algorithm.(publication)

Samsung Research, UK

Machine Learning Research Internship

Machine Learning and Natural Language Processing (NLP)