Abstract
Brain-inspired Hyperdimensional (HD) computing is an emerging technique for low-power/energy designs in many machine learning tasks. Recent works further exploit the low-cost quantized (bipolarized or ternarized) HD model and report dramatic improvements in energy efficiency. However, the quantization loss of HD models leads to a severe drop in classification accuracy. This paper proposes a projection-based quantization framework for HD computing (PQ-HDC) to achieve a flexible and efficient trade-off between accuracy and efficiency. While previous works exploit thresholding-quantization schemes, the proposed PQ-HDC progressively reduces quantization loss using a linear combination of bipolarized HD models. Furthermore, PQ-HDC allows quantization with flexible bit-width while preserving the computational efficiency of the Hamming distance computation. Experimental results on the benchmark dataset demonstrate that PQ-HDC achieves a 2.82% improvement in accuracy over the state-of-the-art method.
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22 June 2021
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Acknowledgments
This work was supported by the Ministry of Science and Technology of Taiwan under Grants MOST 109-2221-E-002-175 and MOST 110-2218-E-002-034 -MBK.
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Huang, CT., Chang, CY., Chuang, YC., Wu, AY.(. (2021). PQ-HDC: Projection-Based Quantization Scheme for Flexible and Efficient Hyperdimensional Computing. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_34
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DOI: https://doi.org/10.1007/978-3-030-79150-6_34
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