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arXiv · 2109.09113

HPTQ: Hardware-Friendly Post Training Quantization

Hai Victor Habi·Reuven Peretz·Elad Cohen·Lior Dikstein·Oranit Dror·Idit Diamant·Roy H. Jennings·Arnon Netzer
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Abstract

Neural network quantization enables the deployment of models on edge devices. An essential requirement for their hardware efficiency is that the quantizers are hardware-friendly: uniform, symmetric, and with power-of-two thresholds. To the best of our knowledge, current post-training quantization methods do not support all of these constraints simultaneously. In this work, we introduce a hardware-friendly post training quantization (HPTQ) framework, which addresses this problem by synergistically combining several known quantization methods. We perform a large-scale study on four tasks: classification, object detection, semantic segmentation and pose estimation over a wide variety of network architectures. Our extensive experiments show that competitive results can be obtained under hardware-friendly constraints.

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Hai Victor Habi, Reuven Peretz, Elad Cohen, Lior Dikstein, Oranit Dror, Idit Diamant, Roy H. Jennings, Arnon Netzer. 2021-09-19. HPTQ: Hardware-Friendly Post Training Quantization. https://arxiv.org/abs/2109.09113

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