A low latency kernel recursive least squares processor using FPGA technology. - Yeyong Pang; Shaojun Wang; Yu Peng 002; Nicholas J. Fraser; Philip Heng Wai Leong (FPT, 2013)

An FPGA-based spectral anomaly detection system. - Duncan J. M. Moss; Zhe Zhang; Nicholas J. Fraser; Philip Heng Wai Leong (FPT, 2014)

A fully pipelined kernel normalised least mean squares processor for accelerated parameter optimisation. - Nicholas J. Fraser; Duncan J. M. Moss; JunKyu Lee; Stephen Tridgell; Craig T. Jin; Philip Heng Wai Leong (FPL, 2015)

Braiding: A scheme for resolving hazards in kernel adaptive filters. - Stephen Tridgell; Duncan J. M. Moss; Nicholas J. Fraser; Philip Heng Wai Leong (FPT, 2015)

Distributed kernel learning using Kernel Recursive Least Squares. - Nicholas J. Fraser; Duncan J. M. Moss; Nicolas Epain; Philip Heng Wai Leong (ICASSP, 2015)

A Microcoded Kernel Recursive Least Squares Processor Using FPGA Technology. - Yeyong Pang; Shaojun Wang; Yu Peng 002; Xiyuan Peng; Nicholas J. Fraser; Philip Heng Wai Leong (ACM Trans. Reconfigurable Technol. Syst., 2016)

FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. - Yaman Umuroglu; Nicholas J. Fraser; Giulio Gambardella; Michaela Blott; Philip Heng Wai Leong; Magnus Jahre; Kees A. Vissers (FPGA, 2017)

Scaling Binarized Neural Networks on Reconfigurable Logic. - Nicholas J. Fraser; Yaman Umuroglu; Giulio Gambardella; Michaela Blott; Philip Heng Wai Leong; Magnus Jahre; Kees A. Vissers (PARMA-DITAM@HiPEAC, 2017)

Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic. - Michaela Blott; Thomas B. Preußer; Nicholas J. Fraser; Giulio Gambardella; Kenneth O'Brien; Yaman Umuroglu; Miriam Leeser (ICCD, 2017)

Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks. - Julian Faraone; Nicholas J. Fraser; Giulio Gambardella; Michaela Blott; Philip Heng Wai Leong (ICONIP (2), 2017)

FPGA Implementations of Kernel Normalised Least Mean Squares Processors. - Nicholas J. Fraser; JunKyu Lee; Duncan J. M. Moss; Julian Faraone; Stephen Tridgell; Craig T. Jin; Philip Heng Wai Leong (ACM Trans. Reconfigurable Technol. Syst., 2017)

Accuracy to Throughput Trade-Offs for Reduced Precision Neural Networks on Reconfigurable Logic. - Jiang Su; Nicholas J. Fraser; Giulio Gambardella; Michaela Blott; Gianluca Durelli; David B. Thomas; Philip Heng Wai Leong; Peter Y. K. Cheung (ARC, 2018)

SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks. - Julian Faraone; Nicholas J. Fraser; Michaela Blott; Philip H. W. Leong (CVPR, 2018)

Inference of quantized neural networks on heterogeneous all-programmable devices. - Thomas B. Preußer; Giulio Gambardella; Nicholas J. Fraser; Michaela Blott (DATE, 2018)

Customizing Low-Precision Deep Neural Networks for FPGAs. - Julian Faraone; Giulio Gambardella; Nicholas J. Fraser; Michaela Blott; Philip H. W. Leong; David Boland (FPL, 2018)

FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. - Michaela Blott; Thomas B. Preußer; Nicholas J. Fraser; Giulio Gambardella; Kenneth O'Brien; Yaman Umuroglu (CoRR, 2018)

FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. - Michaela Blott; Thomas B. Preußer; Nicholas J. Fraser; Giulio Gambardella; Kenneth O'Brien; Yaman Umuroglu; Miriam Leeser; Kees A. Vissers (ACM Trans. Reconfigurable Technol. Syst., 2018)

High-Throughput DNN Inference with LogicNets. - Yaman Umuroglu; Yash Akhauri; Nicholas J. Fraser; Michaela Blott (FCCM, 2020)

Evaluation of Optimized CNNs on FPGA and non-FPGA based Accelerators using a Novel Benchmarking Approach. - Michaela Blott; Johannes Kath; Lisa Halder; Yaman Umuroglu; Nicholas J. Fraser; Giulio Gambardella; Miriam Leeser; Linda Doyle (FPGA, 2020)

LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications. - Yaman Umuroglu; Yash Akhauri; Nicholas James Fraser; Michaela Blott (FPL, 2020)

Memory-Efficient Dataflow Inference for Deep CNNs on FPGA. - Lucian Petrica; Tobias Alonso; Mairin Kroes; Nicholas J. Fraser; Sorin Cotofana; Michaela Blott (FPT, 2020)

FAT: Training Neural Networks for Reliable Inference Under Hardware Faults. - Ussama Zahid; Giulio Gambardella; Nicholas J. Fraser; Michaela Blott; Kees A. Vissers (ITC, 2020)

Kernel Normalised Least Mean Squares with Delayed Model Adaptation. - Nicholas J. Fraser; Philip H. W. Leong (ACM Trans. Reconfigurable Technol. Syst., 2020)

Trainable Preprocessing for Reduced Precision Neural Networks. - Gábor Csordás; Kristof Denolf; Nicholas J. Fraser; Alessandro Pappalardo; Kees A. Vissers (EUSIPCO, 2021)

Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference. - Benjamin Hawks; Javier M. Duarte; Nicholas J. Fraser; Alessandro Pappalardo; Nhan Tran; Yaman Umuroglu (Frontiers Artif. Intell., 2021)

Evaluation of Optimized CNNs on Heterogeneous Accelerators Using a Novel Benchmarking Approach. - Michaela Blott; Nicholas J. Fraser; Giulio Gambardella; Lisa Halder; Johannes Kath; Zachary Neveu; Yaman Umuroglu; Alina Vasilciuc; Miriam Leeser; Linda Doyle (IEEE Trans. Computers, 2021)

Fault-Tolerant Neural Network Accelerators With Selective TMR. - Timoteo García Bertoa; Giulio Gambardella; Nicholas J. Fraser; Michaela Blott; John McAllister (IEEE Des. Test, 2023)

Synthesis of LUT Networks for Random-Looking Dense Functions with Don't Cares - Towards Efficient FPGA Implementation of DNN. - Yukio Miyasaka; Alan Mishchenko; John Wawrzynek; Nicholas J. Fraser (FCCM, 2024)

Greater than the Sum of its LUTs: Scaling Up LUT-based Neural Networks with AmigoLUT. - Olivia Weng; Marta Andronic; Danial Zuberi; Jiaqing Chen; Caleb Geniesse; George A. Constantinides; Nhan Tran; Nicholas J. Fraser; Javier Mauricio Duarte; Ryan Kastner (FPGA, 2025)

Improving Quantization with Post-Training Model Expansion. - Giuseppe Franco; Pablo Monteagudo-Lago; Ian Colbert; Nicholas J. Fraser; Michaela Blott (CoRR, 2025)