Abstract | ||
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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 of 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. |
Year | Venue | Keywords |
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2022 | International Conference on Learning Representations (ICLR) | Neural Network Quantization,Fixed-Point Arithmetic |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 9 |
Name | Order | Citations | PageRank |
---|---|---|---|
Qing Jin | 1 | 0 | 0.34 |
Jian Ren | 2 | 0 | 0.34 |
Richard Zhuang | 3 | 0 | 0.34 |
Sumant Hanumante | 4 | 0 | 0.34 |
Zhengang Li | 5 | 15 | 7.27 |
Zhiyu Chen | 6 | 8 | 1.59 |
Yanzhi Wang | 7 | 1082 | 136.11 |
Kuiyuan Yang | 8 | 148 | 20.89 |
Sergey Tulyakov | 9 | 0 | 1.01 |