用低差异序列提升随机与高维计算的精度与能效
Improved Data Encoding for Emerging Computing Paradigms: From Stochastic to Hyperdimensional Computing
- 用幂次2的范德科普特序列生成随机数，改善相关性
- 实验显示精度显著提升，能耗大幅降低
- 适合资源受限环境下的高效人工智能部署
数据编码是新兴计算范式中的基础步骤，尤其在随机计算（SC）和高维计算（HDC）中，对系统性能与硬件效率起决定性作用。本文提出一种先进编码策略，利用硬件友好的低差异（LD）序列——即基于2的幂次的范德科普特（VDC-2^n）序列——作为随机数生成源。该方法有效缓解了随机性带来的挑战，显著提升了SC与HDC系统的精度与效率，同时改善了序列相关性并降低了硬件复杂度。实验结果表明，该方案在SC与HDC系统中均实现显著精度提升与能效优化，为资源受限环境下集成SC与HDC提供了稳健框架，推动高效可扩展的人工智能实现。
原文摘要 · Abstract (English)
Data encoding is a fundamental step in emerging computing paradigms, particularly in stochastic computing (SC) and hyperdimensional computing (HDC), where it plays a crucial role in determining the overall system performance and hardware cost efficiency. This study presents an advanced encoding strategy that leverages a hardware-friendly class of low-discrepancy (LD) sequences, specifically powers-of-2 bases of Van der Corput (VDC) sequences (VDC-2^n), as sources for random number generation. Our approach significantly enhances the accuracy and efficiency of SC and HDC systems by addressing challenges associated with randomness. By employing LD sequences, we improve correlation properties and reduce hardware complexity. Experimental results demonstrate significant improvements in accuracy and energy savings for SC and HDC systems. Our solution provides a robust framework for integrating SC and HDC in resource-constrained environments, paving the way for efficient and scalable AI implementations.
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