Some highlighted publications from researchers affiliated with the center can be found below.


Erwei Wang, James J. Davis, Ruizhe Zhao, Ho-Cheung Ng, Xinyu Niu, Wayne Luk, Peter Y. K. Cheung, George A. Constantinides, “Deep Neural Network Approximation for Custom Hardware: Where We’ve Been, Where We’re Going”, ACM CSUR 2019,

Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis, “Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions”, ACM CSUR 2018,

Arithmetic for Deep Learning

Erwei Wang, James J. Davis, Peter Y. K. Cheung, George A. Constantinides, “LUTNet: Learning FPGA Configurations for Highly Efficient Neural Network Inference”, Includes open-source software at

George A. Constantinides, “Rethinking Arithmetic for Deep Neural Networks”,

Erwei Wang, James J. Davis, Peter Y. K. Cheung, George A. Constantinides, “LUTNet: Rethinking Inference in FPGA Soft Logic”, in Proc. FCCM 2019,

Yiren Zhao, Xitong Gao, Xuan Guo, Junyi Liu, Erwei Wang, Robert Mullins, Peter Cheung, George Constantinides and Chengzhong Xu, “Automatic Generation of Multi-precision Multi-arithmetic CNN Accelerators for FPGAs”, in Proc. FPT 2019.

Architecture for Deep Learning

A. Boutros, S. Yazdanshenas, and V. Betz, “Embracing Diversity: Enhanced DSP Blocks for Low-Precision Deep Learning on FPGAs,” FPL 2018, pp. 1 – 8,

A. Boutros, M. Eldafrawy, S. Yazdanshenas and V. Betz, “Math Doesn’t Have to be Hard: Logic Block Architectures to Enhance Low-Precision Multiply-Accumulate on FPGAs,” FPGA 2019, pp. 94 – 193,

SeyedRamin Rasoulinezhad, Siddhartha, Hao Zhou, Lingli Wang, David Boland, and Philip H.W. Leong. LUXOR: an FPGA logic cell architecture for efficient compressor tree implementations. FPGA 2020.

Mapping Deep Neural Networks to Hardware

C. Zhang, P. Li, G. Sun, Y. Guan, B. Xiao, and J. Cong, “Optimizing FPGA-based accelerator design for deep convolutional neural networks,” In Proc. FPGA, 2015.

Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, and Kees Vissers. FINN: a framework for fast, scalable binarized neural network inference. In Proc. ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA), 65–74. 2017.

Julian Faraone, Nicholas Fraser, Michaela Blott, and Philip H.W. Leong. SYQ: learning symmetric quantization for efficient deep neural networks. In Proc. Computer Vision and Pattern Recognition (CVPR). June 2018.

Stylianos I. Venieris, Christos-Savvas Bouganis, “Mapping Regular and Irregular Convolutional Neural Networks on FPGAs”, IEEE Transactions on Neural Networks and Learning Systems, 30(2), Feb 2019.

Ruizhe Zhao and Wayne Luk, “Efficient Structured Pruning and Architecture Searching for Group Convolution,” Proc. ICCV NEUARCH Workshop, 2019.

M. Vasileiadis, C.-S. Bouganis, G. Stavropoulos and D. Tzovaras, “Optimising 3D-CNN Design Towards Human Pose Estimation on Low Power Devices”, Proc. BMVC 2019.

A. Kouris, S.I. Venieris, and C.-S. Bouganis, “Towards Efficient On-board Deployment of DNNs on Intelligent Autonomous Systems”, Proc. ISVLSI 2019,

A. Montgomerie-Corcoran, S.I. Venieris, and C.-S. Bouganis, “Power-Aware FPGA Mapping of Convolutional Neural Networks”, Proc. ICFPT 2019,

H. Fan, G. Wang, M. Ferianc, X. Niu, and W. Luk, “Static Block Floating-Point Quantization for Convolutional Neural Networks on FPGA”, Proc. ICFPT 2019,

Stephen Tridgell, Martin Kumm, Martin Hardieck, David Boland, Duncan Moss, Peter Zipf, and Philip H. W. Leong. Unrolling ternary neural networks. ACM Trans. Reconfigurable Technol. Syst., 12(4):22:1–22:23, October 2019.

Training on Non-Traditional Architectures

Cheng Luo, Man-Kit Sit , Hongxiang Fan, Shuanglong Liu, Wayne Luk, Ce Guo, Towards Efficient Deep Neural Network Training by FPGA-Based Batch-Level Parallelism, in Proc. FCCM 2019,

Sean Fox, Julian Faraone, David Boland, Kees Vissers, and Philip H.W. Leong. Training deep neural networks in low-precision with high accuracy using FPGAs. In Proc. International Conference on Field Programmable Technology (FPT)


Further publications can be found at:

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