Nicholas J. Fraser

Publication Activity (10 Years)

Years Active: 2013-2023
Publications (10 Years): 32

Top Topics

Kernel Learning

Hardware Architectures

Recursive Least Squares

Top Venues

ACM Trans. Reconfigurable Technol. Syst.

Publications

Timoteo García Bertoa, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott, John McAllister
Fault-Tolerant Neural Network Accelerators With Selective TMR. IEEE Des. Test 40 (2) (2023)
Michaela Blott, Nicholas J. Fraser, Giulio Gambardella, Lisa Halder, Johannes Kath, Zachary Neveu, Yaman Umuroglu, Alina Vasilciuc, Miriam Leeser, Linda Doyle
Evaluation of Optimized CNNs on Heterogeneous Accelerators Using a Novel Benchmarking Approach. IEEE Trans. Computers 70 (10) (2021)
Benjamin Hawks, Javier M. Duarte, Nicholas J. Fraser, Alessandro Pappalardo, Nhan Tran, Yaman Umuroglu
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference. CoRR (2021)
Gábor Csordás, Kristof Denolf, Nicholas J. Fraser, Alessandro Pappalardo, Kees A. Vissers
Trainable Preprocessing for Reduced Precision Neural Networks. EUSIPCO (2021)
Benjamin Hawks, Javier M. Duarte, Nicholas J. Fraser, Alessandro Pappalardo, Nhan Tran, Yaman Umuroglu
Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference. Frontiers Artif. Intell. 4 (2021)
Ussama Zahid, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott, Kees A. Vissers
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults. CoRR (2020)
Michaela Blott, Johannes Kath, Lisa Halder, Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Miriam Leeser, Linda Doyle
Evaluation of Optimized CNNs on FPGA and non-FPGA based Accelerators using a Novel Benchmarking Approach. FPGA (2020)
Yaman Umuroglu, Yash Akhauri, Nicholas J. Fraser, Michaela Blott
High-Throughput DNN Inference with LogicNets. FCCM (2020)
Lucian Petrica, Tobias Alonso, Mairin Kroes, Nicholas J. Fraser, Sorin Cotofana, Michaela Blott
Memory-Efficient Dataflow Inference for Deep CNNs on FPGA. FPT (2020)
Nicholas J. Fraser, Philip H. W. Leong
Kernel Normalised Least Mean Squares with Delayed Model Adaptation. ACM Trans. Reconfigurable Technol. Syst. 13 (2) (2020)
Ussama Zahid, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott, Kees A. Vissers
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults. ITC (2020)
Yaman Umuroglu, Yash Akhauri, Nicholas J. Fraser, Michaela Blott
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications. CoRR (2020)
Lucian Petrica, Tobias Alonso, Mairin Kroes, Nicholas J. Fraser, Sorin Cotofana, Michaela Blott
Memory-Efficient Dataflow Inference for Deep CNNs on FPGA. CoRR (2020)
Jiang Su, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Gianluca Durelli, David B. Thomas, Philip Heng Wai Leong, Peter Y. K. Cheung
Accuracy to Throughput Trade-Offs for Reduced Precision Neural Networks on Reconfigurable Logic. ARC (2018)
Michaela Blott, Thomas B. Preußer, Nicholas J. Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu
FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. CoRR (2018)
Julian Faraone, Nicholas J. Fraser, Michaela Blott, Philip H. W. Leong
SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks. CVPR (2018)
Thomas B. Preußer, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott
Inference of quantized neural networks on heterogeneous all-programmable devices. DATE (2018)
Thomas B. Preußer, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices. CoRR (2018)
Michaela Blott, Thomas B. Preußer, Nicholas J. Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu, Miriam Leeser
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic. CoRR (2018)
Michaela Blott, Thomas B. Preußer, Nicholas J. Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu, Miriam Leeser, Kees A. Vissers
: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks. ACM Trans. Reconfigurable Technol. Syst. 11 (3) (2018)
Jiang Su, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Gianluca Durelli, David B. Thomas, Philip Heng Wai Leong, Peter Y. K. Cheung
Accuracy to Throughput Trade-offs for Reduced Precision Neural Networks on Reconfigurable Logic. CoRR (2018)
Julian Faraone, Nicholas J. Fraser, Michaela Blott, Philip Heng Wai Leong
SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks. CoRR (2018)
Julian Faraone, Giulio Gambardella, Nicholas J. Fraser, Michaela Blott, Philip H. W. Leong, David Boland
Customizing Low-Precision Deep Neural Networks for FPGAs. FPL (2018)
Nicholas J. Fraser, JunKyu Lee, Duncan J. M. Moss, Julian Faraone, Stephen Tridgell, Craig T. Jin, Philip Heng Wai Leong
FPGA Implementations of Kernel Normalised Least Mean Squares Processors. ACM Trans. Reconfigurable Technol. Syst. 10 (4) (2017)
Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, Kees A. Vissers
Scaling Binarized Neural Networks on Reconfigurable Logic. CoRR (2017)
Julian Faraone, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks. ICONIP (2) (2017)
Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, Kees A. Vissers
Scaling Binarized Neural Networks on Reconfigurable Logic. PARMA-DITAM@HiPEAC (2017)
Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, Kees A. Vissers
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. FPGA (2017)
Julian Faraone, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks. CoRR (2017)
Michaela Blott, Thomas B. Preußer, Nicholas J. Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu, Miriam Leeser
Scaling Neural Network Performance through Customized Hardware Architectures on Reconfigurable Logic. ICCD (2017)
Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Heng Wai Leong, Magnus Jahre, Kees A. Vissers
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. CoRR (2016)
Yeyong Pang, Shaojun Wang, Yu Peng, Xiyuan Peng, Nicholas J. Fraser, Philip Heng Wai Leong
A Microcoded Kernel Recursive Least Squares Processor Using FPGA Technology. ACM Trans. Reconfigurable Technol. Syst. 10 (1) (2016)
Stephen Tridgell, Duncan J. M. Moss, Nicholas J. Fraser, Philip Heng Wai Leong
Braiding: A scheme for resolving hazards in kernel adaptive filters. FPT (2015)
Nicholas J. Fraser, Duncan J. M. Moss, Nicolas Epain, Philip Heng Wai Leong
Distributed kernel learning using Kernel Recursive Least Squares. ICASSP (2015)
Nicholas J. Fraser, Duncan J. M. Moss, JunKyu Lee, Stephen Tridgell, Craig T. Jin, Philip Heng Wai Leong
A fully pipelined kernel normalised least mean squares processor for accelerated parameter optimisation. FPL (2015)
Duncan J. M. Moss, Zhe Zhang, Nicholas J. Fraser, Philip Heng Wai Leong
An FPGA-based spectral anomaly detection system. FPT (2014)
Yeyong Pang, Shaojun Wang, Yu Peng, Nicholas J. Fraser, Philip Heng Wai Leong
A low latency kernel recursive least squares processor using FPGA technology. FPT (2013)