Stochastic computing offers a probabilistic approach to address challenges posed by problems with uncertainty and noise in various fields, particularly machine learning. The realization of stochastic computing, however, faces the limitation of developing reliable stochastic logics. Here, we present stochastic logics development using memristors. Specifically, we integrate memristors into logic circuits to design the stochastic logics, wherein the inherent stochasticity in memristor switching is harnessed to enable stochastic number encoding and processing with well-regulated probabilities and correlations. As a practical application of the stochastic logics, we design a compact stochastic Roberts cross operator for edge detection. Remarkably, the operator demonstrates exceptional contour and texture extractions, even in the presence of 50% noise, and owning to the probabilistic nature and compact design, the operator can consume 95% less computational costs required by conventional binary computing. The results underscore the great potential of our stochastic computing approach as a lightweight local solution to machine learning challenges in autonomous driving, virtual reality, medical diagnosis, industrial automation, and beyond.

通过将忆阻器集成到逻辑电路中，我们开发了一种随机逻辑的方法，其中利用了忆阻器开关的内在随机性来实现具有良好概率和相关性的随机数编码和处理。作为一种实际应用，我们设计了一个紧凑的随机Roberts交叉算子用于边缘检测，该算子在50％噪声存在的情况下展现了出色的轮廓和纹理提取性能，并且由于概率性质和紧凑设计，该算子所需的计算成本比传统二进制计算低95％。这些结果突显了我们的随机计算方法在自动驾驶、虚拟现实、医学诊断、工业自动化等领域中作为轻量级本地解决方案的巨大潜力。

基于 memristor 启用的随机逻辑的本地随机计算