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：摘要：随机计算（SC）是一种非常规计算范式，以随机比特流的形式处理数据。SC系统的准确性和能效高度依赖于随机数生成器（SNG）单元，该单元将数据从传统二进制转换为随机比特流。近期研究表明，通过在SNG单元中使用Sobol序列和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中的应用仍有待探索。本文研究了一些适用于SC的新型随机序列。通过设计空间探索，我们提出了一种有望实现高精度、高能效SC的随机数生成器。我们提出了P2LSG，一种基于2的幂次Van der Corput（VDC）序列的低成本、高能效低差异序列生成器。我们通过两个SC图像与视频处理案例（图像缩放和场景融合）评估了该新型比特流生成器的性能。针对场景融合任务，我们首次提出了全新的SC设计方案。实验结果表明，与现有最优方法相比，该方法具有更高的准确性，同时降低了硬件成本和能耗。