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Duncan JM Moss
Duncan JM Moss
Megh Computing, PhD
Verified email at meghcomputing.com
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Cited by
Cited by
Year
Can FPGAs beat GPUs in accelerating next-generation deep neural networks?
E Nurvitadhi, G Venkatesh, J Sim, D Marr, R Huang, J Ong Gee Hock, ...
Proceedings of the 2017 ACM/SIGDA international symposium on field …, 2017
5592017
A customizable matrix multiplication framework for the intel harpv2 xeon+ fpga platform: A deep learning case study
DJM Moss, S Krishnan, E Nurvitadhi, P Ratuszniak, C Johnson, J Sim, ...
Proceedings of the 2018 ACM/SIGDA International Symposium on Field …, 2018
982018
High performance binary neural networks on the Xeon+ FPGA™ platform
DJM Moss, E Nurvitadhi, J Sim, A Mishra, D Marr, S Subhaschandra, ...
2017 27Th International conference on field programmable logic and …, 2017
932017
A two-speed, radix-4, serial–parallel multiplier
DJM Moss, D Boland, PHW Leong
IEEE Transactions on Very Large Scale Integration (VLSI) Systems 27 (4), 769-777, 2018
512018
Rolling window time series prediction using MapReduce
L Li, F Noorian, DJM Moss, PHW Leong
Proceedings of the 2014 IEEE 15th international conference on information …, 2014
412014
High-dimensional time series prediction using kernel-based Koopman mode regression
JC Hua, F Noorian, D Moss, PHW Leong, GH Gunaratne
Nonlinear Dynamics 90, 1785-1806, 2017
322017
Unrolling ternary neural networks
S Tridgell, M Kumm, M Hardieck, D Boland, D Moss, P Zipf, PHW Leong
ACM Transactions on Reconfigurable Technology and Systems (TRETS) 12 (4), 1-23, 2019
282019
Real-time FPGA-based anomaly detection for radio frequency signals
DJM Moss, D Boland, P Pourbeik, PHW Leong
2018 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2018
172018
Customizable FPGA OpenCL matrix multiply design template for deep neural networks
J Yinger, E Nurvitadhi, D Capalija, A Ling, D Marr, S Krishnan, D Moss, ...
2017 International Conference on Field Programmable Technology (ICFPT), 259-262, 2017
172017
Long short-term memory for radio frequency spectral prediction and its real-time FPGA implementation
YH Lee, DJM Moss, J Faraone, P Blackmore, D Salmond, D Boland, ...
MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), 1-9, 2018
122018
A fully pipelined kernel normalised least mean squares processor for accelerated parameter optimisation
NJ Fraser, DJM Moss, JK Lee, S Tridgell, CT Jin, PHW Leong
2015 25th International Conference on Field Programmable Logic and …, 2015
102015
An FPGA-based spectral anomaly detection system
DJM Moss, Z Zhang, NJ Fräser, PHW Leong
2014 International Conference on Field-Programmable Technology (FPT), 175-182, 2014
102014
Braiding: A scheme for resolving hazards in kernel adaptive filters
S Tridgell, DJM Moss, NJ Fraser, PHW Leong
2015 International Conference on Field Programmable Technology (FPT), 136-143, 2015
82015
FPGA implementations of kernel normalised least mean squares processors
NJ Fraser, J Lee, DJM Moss, J Faraone, S Tridgell, CT Jin, PHW Leong
ACM Transactions on Reconfigurable Technology and Systems (TRETS) 10 (4), 1-20, 2017
52017
Accelerator templates and runtime support for variable precision CNN
S Krishnan, P Ratusziak, C Johnson, D Moss, S Subhaschandra
CISC Workshop, 2017
42017
Inferencer graph for implementing machine learning model topology
D Moss
US Patent App. 17/025,871, 2022
22022
Circuitry for low-precision deep learning
M Langhammer, S Srinivasan, GW Baeckler, D Moss, S Avancha, D Das
US Patent 11,275,998, 2022
22022
Distributed kernel learning using kernel recursive least squares
NJ Fraser, DJM Moss, N Epain, PHW Leong
2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015
12015
Directed acyclic graph template for data pipeline
S Subhaschandra, J Beare, D Moss
US Patent 11,405,312, 2022
2022
FPGA Architectures for Low Precision Machine Learning
DJM Moss
University of Sydney, 2018
2018
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Articles 1–20