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

2025-01-06

AI Summary

To address key bottlenecks hindering stochastic computing (SC) and hyperdimensional computing (HDC) on resource-constrained edge devices—including poor-quality random number generation, high encoding correlation, and excessive hardware overhead—this paper introduces, for the first time, a hardware-friendly deterministic encoding scheme based on the base-$2^n$ Van der Corput (VDC-$2^n$) low-discrepancy sequence for SC/HDC data representation. The proposed method achieves strong decorrelation, controllable stochasticity, and minimal logic overhead, thereby unifying computational predictability with statistical robustness. Experimental evaluation across multiple benchmark tasks demonstrates that, compared to baseline approaches, the method improves accuracy by 12.7% and reduces energy consumption by 38.4%. These gains significantly enhance the practicality, energy efficiency, and scalability of SC/HDC in edge AI applications.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Hardware-aware ML
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Resource-limited environments
• Random number generation
• Computational accuracy and efficiency

Innovation

• VDC Sequence
• Hardware-friendly Random Numbers
• Energy-efficient Computing