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arXiv · 1807.09760

Method for Hybrid Precision Convolutional Neural Network Representation

Mo'taz Al-Hami·Marcin Pietron·Rishi Kumar·Raul A. Casas·Samer L. Hijazi·Chris Rowen
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Abstract

This invention addresses fixed-point representations of convolutional neural networks (CNN) in integrated circuits. When quantizing a CNN for a practical implementation there is a trade-off between the precision used for operations between coefficients and data and the accuracy of the system. A homogenous representation may not be sufficient to achieve the best level of performance at a reasonable cost in implementation complexity or power consumption. Parsimonious ways of representing data and coefficients are needed to improve power efficiency and throughput while maintaining accuracy of a CNN.

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Mo'taz Al-Hami, Marcin Pietron, Rishi Kumar, Raul A. Casas, Samer L. Hijazi, Chris Rowen. 2018-07-24. Method for Hybrid Precision Convolutional Neural Network Representation. https://arxiv.org/abs/1807.09760

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