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Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks by Wu, Chen; Wang, Mingyu; Li, Xiayu; Lu, Jicheng; Wang, Kun; He, Lei is a scholarly article available to read on EtoBox.

What is Phoenix: A Low-Precision Floating-Point Quantization Oriented Architecture for Convolutional Neural Networks about?

Convolutional neural networks (CNNs) achieve state-of-the-art performance at the cost of becoming deeper and larger. Although quantization (both fixed-point and floating-point) has proven effective for reducing storage and memory access, two challenges -- 1) accuracy loss caused by quantization without calibration, fine-tuning or re-training for deep CNNs and 2) hardware inefficiency caused by floating-point quantization -- prevent processors from completely leveraging the benefits. In this paper, we propose a low-precision floating-point quantization oriented processor, named Phoenix, to address the above challenges. We primarily have three key observations: 1) 8-bit floating-point quantization incurs less error than 8-bit fixed-point quantization; 2) without using any calibration, fine-tuning or re-training techniques, normalization before quantization further reduces accuracy degradation; 3) 8-bit floating-point multiplier achieves higher hardware efficiency than 8-bit fixed-point multiplier if the full-precision product is applied. Based on these key observations, we propose a normalization-oriented 8-bit floating-point quantization method to reduce storage and memory access wi

Author

Wu, Chen; Wang, Mingyu; Li, Xiayu; Lu, Jicheng; Wang, Kun; He, Lei

Published

2020

Language

EN