Improved Data Encoding for Emerging Computing Paradigms: From Stochastic to Hyperdimensional Computing

• URL: http://arxiv.org/abs/2501.02715v1
• Date: Mon, 06 Jan 2025 02:07:49 GMT
• Title: Improved Data Encoding for Emerging Computing Paradigms: From Stochastic to Hyperdimensional Computing
• Abstract summary: This study presents an advanced encoding strategy that leverages a hardware-friendly class of low-discrepancy (LD) sequences.<n>Our approach significantly enhances the accuracy and efficiency of SC and HDC systems by addressing challenges associated with randomness.<n>Our solution provides a robust framework for integrating SC and HDC in resource-constrained environments, paving the way for efficient and scalable AI implementations.
• Score: 1.523100574874007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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