Imperial College London


Faculty of EngineeringDepartment of Computing

Professor of Computer Engineering



+44 (0)20 7594 8313w.luk Website




434Huxley BuildingSouth Kensington Campus






BibTex format

author = {Zhao, R and Niu, X and Wu, Y and Luk, W and Liu, Q},
doi = {10.1007/978-3-319-56258-2_22},
pages = {255--267},
publisher = {Springer},
title = {Optimizing CNN-based object detection algorithms on embedded FPGA platforms},
url = {},
year = {2017}

RIS format (EndNote, RefMan)

AB - Algorithms based on Convolutional Neural Network (CNN) have recently been applied to object detection applications, greatly improving their performance. However, many devices intended for these algorithms have limited computation resources and strict power consumption constraints, and are not suitable for algorithms designed for GPU workstations. This paper presents a novel method to optimise CNNbased object detection algorithms targeting embedded FPGA platforms. Given parameterised CNN hardware modules, an optimisation flow takes network architectures and resource constraints as input, and tunes hardware parameters with algorithm-specific information to explore the design space and achieve high performance. The evaluation shows that our design model accuracy is above 85% and, with optimised configuration, our design can achieve 49.6 times speed-up compared with software implementation.
AU - Zhao,R
AU - Niu,X
AU - Wu,Y
AU - Luk,W
AU - Liu,Q
DO - 10.1007/978-3-319-56258-2_22
EP - 267
PB - Springer
PY - 2017///
SN - 0302-9743
SP - 255
TI - Optimizing CNN-based object detection algorithms on embedded FPGA platforms
UR -
UR -
ER -