Subtensor Quantization for Mobilenets
| Autor: | Vasilios Daskalopoulos, Andrey Melnikov, Thu Dinh, Sek M. Chai |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Computer Vision – ECCV 2020 Workshops ISBN: 9783030682378 ECCV Workshops (5) |
| Popis: | Quantization for deep neural networks (DNN) have enabled developers to deploy models with less memory and more efficient low-power inference. However, not all DNN designs are friendly to quantization. For example, the popular Mobilenet architecture has been tuned to reduce parameter size and computational latency with separable depthwise convolutions, but not all quantization algorithms work well and the accuracy can suffer against its float point versions. In this paper, we analyzed several root causes of quantization loss and proposed alternatives that do not rely on per-channel or training-aware approaches. We evaluate the image classification task on ImageNet dataset, and our post-training quantized 8-bit inference top-1 accuracy in within 0.7% of the floating point version. |
| Databáze: | OpenAIRE |
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