P
Philip H. W. Leong
Researcher at University of Sydney
Publications - 280
Citations - 7574
Philip H. W. Leong is an academic researcher from University of Sydney. The author has contributed to research in topics: Field-programmable gate array & Artificial neural network. The author has an hindex of 42, co-authored 268 publications receiving 6554 citations. Previous affiliations of Philip H. W. Leong include University of British Columbia & The Chinese University of Hong Kong.
Papers
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Proceedings ArticleDOI
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
Yaman Umuroglu,Nicholas J. Fraser,Giulio Gambardella,Michaela Blott,Philip H. W. Leong,Magnus Jahre,Kees Vissers +6 more
TL;DR: FINN, a framework for building fast and flexible FPGA accelerators using a flexible heterogeneous streaming architecture that implements fully connected, convolutional and pooling layers, with per-layer compute resources being tailored to user-provided throughput requirements is presented.
Journal ArticleDOI
A laser-micromachined multi-modal resonating power transducer for wireless sensing systems
TL;DR: In this article, the authors presented a vibration-induced power generator with total volume of ∼1 cm 3 which uses laser-micromachined springs to convert mechanical energy into useful electrical power by Faraday's law of induction.
Journal ArticleDOI
Gaussian random number generators
TL;DR: The algorithms underlying various GRNGs are described, their computational requirements are compared, and the quality of the random numbers are examined with emphasis on the behaviour in the tail region of the Gaussian probability density function.
Journal ArticleDOI
The nature and distribution of errors in sound localization by human listeners
TL;DR: The ability of human subjects to localize a short noise burst presented in the free field with the subject indicating the perceived location by pointing their nose towards the source by using a closed loop training paradigm.
Proceedings ArticleDOI
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
Yaman Umuroglu,Nicholas J. Fraser,Giulio Gambardella,Michaela Blott,Philip H. W. Leong,Magnus Jahre,Kees Vissers +6 more
TL;DR: In this article, the authors present FINN, a framework for building fast and flexible FPGA accelerators using a flexible heterogeneous streaming architecture, with fully connected, convolutional and pooling layers, with per-layer compute resources being tailored to user-provided throughput requirements.