In situ training of feed-forward and recurrent convolutional memristor networks

Abstract

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 memristors encoded both spatial and temporal translational invariance simultaneously in a convolutional long short-term memory network—a memristor-based neural network with intrinsic 3D input processing—which was trained in situ to classify a synthetic MNIST sequence dataset using just 850 weights. These proof-of-principle demonstrations combine the architectural advantages of weight sharing and the area/energy efficiency boost of the memristors, paving the way to future edge artificial intelligence. Memristive devices can provide energy-efficient neural network implementations, but they must be tailored to suit different network architectures. Wang et al. develop a trainable weight-sharing mechanism for memristor-based CNNs and ConvLSTMs, achieving a 75% reduction in weights without compromising accuracy.