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arXiv · 1805.08743

CascadeCNN: Pushing the performance limits of quantisation

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, to perform high-throughput inference by exploiting the computation time-accuracy trade-off. Without the need for retraining, a two-stage architecture tailored for any given FPGA device is generated, consisting of a low- and a high-precision unit. A confidence evaluation unit is employed between them to identify misclassified cases at run time and forward them to the high-precision unit or terminate computation. Experiments demonstrate that CascadeCNN achieves a performance boost of up to 55% for VGG-16 and 48% for AlexNet over the baseline design for the same resource budget and accuracy.

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Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis. 2018-05-22. CascadeCNN: Pushing the performance limits of quantisation. https://arxiv.org/abs/1805.08743

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