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 https://github.com/huqinghao/PalQuant.
Q. Hu, G. Li and Q. Wu—Equal Contribution.
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Notes
- 1.
For example, given the 4-bit number x, the lowest 2-bit number changes from 3 to 0 when x changes from 3 to 4 (\(\left[ 0\ 0\ 1 \ \ 1\right] \rightarrow \left[ 0\ 1\ 0 \ 0\right] \)).
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Acknowledgements
This work was supported in part by National Key Research and Development Program of China (Grant No. 2021ZD0201504), and National Natural Science Foundation of China (Grant No. 62106267).
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Hu, Q., Li, G., Wu, Q., Cheng, J. (2022). PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13671. Springer, Cham. https://doi.org/10.1007/978-3-031-20083-0_19
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