Song Lekai, Liu Pengyu, Pei Jingfang, Liu Yang, Liu Songwei, Wang Shengbo, Ng Leonard W T, Hasan Tawfique, Pun Kong-Pang, Gao Shuo, Hu Guohua
Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
Nat Commun. 2025 May 16;16(1):4550. doi: 10.1038/s41467-025-59872-2.
The demand for efficient edge computer vision has spurred the development of stochastic computing for image processing. Memristors, by introducing their inherent switching stochasticity into computation, readily enable stochastic image processing. Here, we present a lightweight, error-tolerant edge detection approach based on memristor-enabled stochastic computing. By integrating memristors into compact logic circuits, we realise lightweight stochastic logics for stochastic number encoding and processing with well-regulated probabilities and correlations. This stochastic and probabilistic computational nature allows the stochastic logics to perform edge detection in edge visual scenarios characterised by high-level errors. As a demonstration, we implement a hardware edge detection operator using the stochastic logics, and prove its exceptional performance with 95% less energy consumption while withstanding 50% bit-flips. The results underscore the potential of our stochastic edge detection approach for developing efficient edge visual hardware for autonomous driving, virtual and augmented reality, medical imaging diagnosis, and beyond.
对高效边缘计算机视觉的需求推动了用于图像处理的随机计算的发展。忆阻器通过将其固有的开关随机性引入计算中，很容易实现随机图像处理。在此，我们提出一种基于忆阻器的随机计算的轻量级、容错边缘检测方法。通过将忆阻器集成到紧凑的逻辑电路中，我们实现了用于随机数编码和处理的轻量级随机逻辑，其概率和相关性得到良好调节。这种随机和概率计算性质使得随机逻辑能够在以高级错误为特征的边缘视觉场景中执行边缘检测。作为演示，我们使用随机逻辑实现了一个硬件边缘检测算子，并证明了其卓越性能，能耗降低95%，同时能承受50%的位翻转。这些结果强调了我们的随机边缘检测方法在为自动驾驶、虚拟现实和增强现实、医学成像诊断等开发高效边缘视觉硬件方面的潜力。
