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The explosive growth of machine learning is largely due to the recent advancements in hardware and architecture. The engineering of network structures, taking advantage of the spatial or temporal translational isometry of patterns, naturally leads to bio-inspired, shared-weight structures such as convolutional neural networks, which have markedly reduced the number of free parameters. State-of-the-art microarchitectures commonly rely on weight-sharing techniques, but still suffer from the von Neumann bottleneck of transistor-based platforms. Here, we experimentally demonstrate the in situ training of a five-level convolutional neural network that self-adapts to non-idealities of the one-transistor one-memristor array to classify the MNIST dataset, achieving similar accuracy to the memristor-based multilayer perceptron with a reduction in trainable parameters of ~75% owing to the shared weights. In addition, the …