Towards Stochastic Computing: In Situ Stochastic Synapses for High‐Generalization and High‐Efficiency Neuromorphic Computing

Stochastic computing is crucial for the accurate and efficient processing of massive data in advanced artificial intelligence applications. However, performing stochastic computing that balances generalization and efficiency remains a significant challenge. Here, we propose an in situ stochastic synapse (In‐SSS) that can achieve in‐memory stochastic computing with tunable probability from 0 to 1 within a single device. The device supports an innovative stochastic computing method, enabling in situ stochastic computing within the computation‐in‐memory architecture. Most importantly, it allows dynamic switching of stochasticity between the forward phase and weight update phase, effectively addressing the conundrum of balancing generalization and efficiency during stochastic computing. With these characteristics, the In‐SSS is used to implement a novel stochastic neural network, achieving noticeable improvements in generalization ability and accuracy along with a respectable reduction in computation, compared with the fully connected neural network. The generality and robustness of the proposed method have also been demonstrated. This three‐in‐one device paradigm, integrating random number generation, weight memorization and efficient computation, provides a cutting‐edge strategy for designing next‐generation neuromorphic devices toward stochastic computing.