Analysis and Solution of CNN Accuracy Reduction over Channel Loop Tiling
PROCEEDINGS OF THE 2020 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2020)(2020)
摘要
Owing to the growth of the size of convolutional neural networks (CNNs), quantization and loop tiling (also called loop breaking) are mandatory to implement CNN on an embedded system. However, channel loop tiling of quantized CNNs induces unexpected errors. We explain why channel loop tiling of quantized CNNs induces the unexpected errors, and how the errors affect the accuracy of state-of-the-art CNNs. We also propose a method to recover accuracy under channel tiling by compressing and decompressing the most-significant bits of partial sums. Using the proposed method, we can recover accuracy by 12.3% with only 1% circuit area overhead and an additional 2% of power consumption.
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关键词
CNN accuracy reduction,channel loop tiling,loop breaking,quantized CNN,unexpected errors,channel tiling,convolutional neural networks
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