Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices

• URL: http://arxiv.org/abs/2103.06709v1
• Date: Mon, 8 Mar 2021 05:25:45 GMT
• Title: Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices
• Authors: Toygun Basaklar, Yigit Tuncel, Shruti Yadav Narayana, Suat Gumussoy, and Umit Y. Ogras
• Abstract summary: This paper presents a technique to minimize the hypervector dimension while maintaining the accuracy and improving the robustness of the classifier. The proposed approach decreases the hypervector dimension by more than $32times$ while maintaining or increasing the accuracy achieved by conventional HDC. Experiments on a commercial hardware platform show that the proposed approach achieves more than one order of magnitude reduction in model size, inference time, and energy consumption.
• Score: 0.20971479389679334
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Hyperdimensional computing (HDC) has emerged as a new light-weight learning algorithm with smaller computation and energy requirements compared to conventional techniques. In HDC, data points are represented by high-dimensional vectors (hypervectors), which are mapped to high-dimensional space (hyperspace). Typically, a large hypervector dimension ($\geq1000$) is required to achieve accuracies comparable to conventional alternatives. However, unnecessarily large hypervectors increase hardware and energy costs, which can undermine their benefits. This paper presents a technique to minimize the hypervector dimension while maintaining the accuracy and improving the robustness of the classifier. To this end, we formulate the hypervector design as a multi-objective optimization problem for the first time in the literature. The proposed approach decreases the hypervector dimension by more than $32\times$ while maintaining or increasing the accuracy achieved by conventional HDC. Experiments on a commercial hardware platform show that the proposed approach achieves more than one order of magnitude reduction in model size, inference time, and energy consumption. We also demonstrate the trade-off between accuracy and robustness to noise and provide Pareto front solutions as a design parameter in our hypervector design.

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