PalQuant: Accelerating High-precision Networks on Low-precision Accelerators

• URL: http://arxiv.org/abs/2208.01944v1
• Date: Wed, 3 Aug 2022 09:44:13 GMT
• Title: PalQuant: Accelerating High-precision Networks on Low-precision Accelerators
• Authors: Qinghao Hu, Gang Li, Qiman Wu, Jian Cheng
• Abstract summary: Low-precision deep learning accelerators (DLAs) have become popular due to their advantages in chip area and energy consumption. One way to achieve both high accuracy and efficient inference is to deploy high-precision neural networks on low-precision DLAs. We propose the PArallel Low-precision Quantization (PalQuant) method that approximates high-precision computations via learning parallel low-precision representations from scratch.
• Score: 17.877271678887315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently low-precision deep learning accelerators (DLAs) have become popular due to their advantages in chip area and energy consumption, yet the low-precision quantized models on these DLAs bring in severe accuracy degradation. One way to achieve both high accuracy and efficient inference is to deploy high-precision neural networks on low-precision DLAs, which is rarely studied. In this paper, we propose the PArallel Low-precision Quantization (PalQuant) method that approximates high-precision computations via learning parallel low-precision representations from scratch. In addition, we present a novel cyclic shuffle module to boost the cross-group information communication between parallel low-precision groups. Extensive experiments demonstrate that PalQuant has superior performance to state-of-the-art quantization methods in both accuracy and inference speed, e.g., for ResNet-18 network quantization, PalQuant can obtain 0.52\% higher accuracy and 1.78$\times$ speedup simultaneously over their 4-bit counter-part on a state-of-the-art 2-bit accelerator. Code is available at \url{https://github.com/huqinghao/PalQuant}.

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