Search Results for author: Philip Leong

Found 5 papers, 1 papers with code

A Block Minifloat Representation for Training Deep Neural Networks

no code implementations ICLR 2021 Sean Fox, Seyedramin Rasoulinezhad, Julian Faraone, David Boland, Philip Leong

Training Deep Neural Networks (DNN) with high efficiency can be difficult to achieve with native floating point representations and commercially available hardware.

Monte Carlo Deep Neural Network Arithmetic

no code implementations25 Sep 2019 Julian Faraone, Philip Leong

We present a novel technique, Monte Carlo Deep Neural Network Arithmetic (MCA), for determining the sensitivity of Deep Neural Networks to quantization in floating point arithmetic. We do this by applying Monte Carlo Arithmetic to the inference computation and analyzing the relative standard deviation of the neural network loss.

Image Classification Quantization

Scaling Binarized Neural Networks on Reconfigurable Logic

no code implementations12 Jan 2017 Nicholas J. Fraser, Yaman Umuroglu, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers

Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost.

General Classification

FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

4 code implementations1 Dec 2016 Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip Leong, Magnus Jahre, Kees Vissers

Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values.

General Classification

Feature Graph Architectures

no code implementations15 Dec 2013 Richard Davis, Sanjay Chawla, Philip Leong

In this article we propose feature graph architectures (FGA), which are deep learning systems employing a structured initialisation and training method based on a feature graph which facilitates improved generalisation performance compared with a standard shallow architecture.

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