Resistive memory technologies could be used to create intelligent systems that learn locally at the edge. However, current approaches typically use learning algorithms that cannot be reconciled with the intrinsic non-idealities of resistive memory, particularly cycle-to-cycle variability. Here, we report a machine learning scheme that exploits memristor variability to implement Markov chain Monte Carlo sampling in a fabricated array of 16,384 devices configured as a Bayesian machine learning model. We apply the approach experimentally to carry out malignant tissue recognition and heart arrhythmia detection tasks, and, using a calibrated simulator, address the cartpole reinforcement learning task. Our approach demonstrates robustness to device degradation at ten million endurance cycles, and, based on circuit and system-level simulations, the total energy required to train the models is estimated to be on the order of microjoules, which is notably lower than in complementary metal–oxide–semiconductor (CMOS)-based approaches.

电阻式存储器技术可用于创建在边缘本地学习的智能系统。然而，当前的方法通常使用无法与电阻式存储器的内在非理想性相协调的学习算法，特别是周期到周期的可变性。在这里，我们报告了一种机器学习方案，该方案利用忆阻器可变性在配置为贝叶斯机器学习模型的 16,384 个设备的制造阵列中实现马尔可夫链蒙特卡罗采样。我们通过实验应用该方法来执行恶性组织识别和心律失常检测任务，并使用经过校准的模拟器来解决cartpole 强化学习任务。我们的方法展示了在一千万次耐久性循环下对设备退化的鲁棒性，并且基于电路和系统级仿真，