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.

本研究解决了新兴计算范式中数据编码的关键问题，特别是在随机计算（SC）和超维计算（HDC）中的应用。通过利用与硬件友好的低差异（LD）序列的高级编码策略，我们显著提高了系统的准确性和效率，并减少了硬件复杂性。实验结果表明，该方法在能效和准确性方面取得了显著改进，为资源受限环境下的SC和HDC集成提供了可靠框架，促进了高效可扩展的人工智能实施。

针对新兴计算范式的改进数据编码：从随机计算到超维计算