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arXiv · 1807.05053

CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks

Alexandros Kouris·Stylianos I. Venieris·Christos-Savvas Bouganis
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Abstract

This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference. A two-stage architecture tailored for any given CNN-FPGA pair is generated, consisting of a low- and high-precision unit in a cascade. A confidence evaluation unit is employed to identify misclassified cases from the excessively low-precision unit and forward them to the high-precision unit for re-processing. Experiments demonstrate that the proposed toolflow can achieve a performance boost up to 55% for VGG-16 and 48% for AlexNet over the baseline design for the same resource budget and accuracy, without the need of retraining the model or accessing the training data.

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Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis. 2018-07-13. CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks. https://arxiv.org/abs/1807.05053

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