F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization

Qing Jin · Jian Ren · Richard Zhuang · Sumant Hanumante · Zhengang Li · Zhiyu Chen · Yanzhi Wang · Kaiyuan Yang · Sergey Tulyakov

[ Abstract ]
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Wed 27 Apr 6:30 p.m. PDT — 8:30 p.m. PDT
Oral presentation: Oral 3: Learning from distribution shift
Wed 27 Apr 9 a.m. PDT — 10:30 a.m. PDT


Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there is a performance gap between quantized and full-precision models. To reduce it, existing quantization approaches require high-precision INT32 or full-precision multiplication during inference for scaling or dequantization. This introduces a noticeable cost in terms of memory, speed, and required energy. To tackle these issues, we present F8Net, a novel quantization framework consisting in only fixed-point 8-bit multiplication. To derive our method, we first discuss the advantages of fixed-point multiplication with different formats of fixed-point numbers and study the statistical behavior of the associated fixed-point numbers. Second, based on the statistical and algorithmic analysis, we apply different fixed-point formats for weights and activations of different layers. We introduce a novel algorithm to automatically determine the right format for each layer during training. Third, we analyze a previous quantization algorithm—parameterized clipping activation (PACT)—and reformulate it using fixed-point arithmetic. Finally, we unify the recently proposed method for quantization fine-tuning and our fixed-point approach to show the potential of our method. We verify F8Net on ImageNet for MobileNet V1/V2 and ResNet18/50. Our approach achieves comparable and better performance, when compared not only to existing quantization techniques with INT32 multiplication or floating point arithmetic, but also to the full-precision counterparts, achieving state-of-the-art performance.

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