Stochastic Computing (SC) is an unconventional computing paradigm processing data in the form of random bit-streams. The accuracy and energy efficiency of SC systems highly depend on the stochastic number generator (SNG) unit that converts the data from conventional binary to stochastic bit-streams. Recent work has shown significant improvement in the efficiency of SC systems by employing low-discrepancy (LD) sequences such as Sobol and Halton sequences in the SNG unit. Still, the usage of many well-known random sequences for SC remains unexplored. This work studies some new random sequences for potential application in SC. Our design space exploration proposes a promising random number generator for accurate and energy-efficient SC. We propose P2LSG, a low-cost and energy-efficient Low-discrepancy Sequence Generator derived from Powers-of-2 VDC (Van der Corput) sequences. We evaluate the performance of our novel bit-stream generator for two SC image and video processing case studies: image scaling and scene merging. For the scene merging task, we propose a novel SC design for the first time. Our experimental results show higher accuracy and lower hardware cost and energy consumption compared to the state-of-the-art.

翻译：随机计算（SC）是一种以随机比特流形式处理数据的非传统计算范式。SC系统的精度与能效高度依赖于将传统二进制数据转换为随机比特流的随机数生成器（SNG）单元。近年研究表明，在SNG单元中采用Sobol序列、Halton序列等低差异（LD）序列可显著提升SC系统的效率。然而，许多已知随机序列在SC中的应用尚未被充分探索。本文研究了几种适用于SC的新型随机序列。通过设计空间探索，我们提出了一种面向高精度高能效SC的随机数生成方案。我们设计了一种基于2的幂次范德科普特（Van der Corput, VDC）序列的低成本、高能效低差异序列生成器P2LSG。针对图像缩放与场景融合两项SC图像/视频处理案例，我们评估了新型比特流生成器的性能。其中，在场景融合任务中，我们首次提出了新型SC设计。实验结果表明，与现有最优方案相比，本方法在提升精度的同时降低了硬件成本与能耗。