Improved Data Encoding for Emerging Computing Paradigms: From Stochastic to Hyperdimensional Computing
This work addresses efficiency challenges in emerging computing paradigms for resource-constrained AI implementations, representing an incremental improvement.
The study tackled the problem of data encoding in stochastic and hyperdimensional computing by introducing an advanced encoding strategy using low-discrepancy sequences, resulting in significant improvements in accuracy and energy savings for these systems.
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.