A memristive deep belief neural network based on silicon synapses

Memristor-based neuromorphic computing could overcome the limitations of traditional von Neumann computing architectures-in which data are shuffled between separate memory and processing units-and improve the performance of deep neural networks. However, this will require accurate synaptic-like device performance, and memristors typically suffer from poor yield and a limited number of reliable conductance states. Here we report floating-gate memristive synaptic devices that are fabricated in a commercial complementary metal-oxide-semiconductor process. These silicon synapses offer analogue tunability, high endurance, long retention time, predictable cycling degradation, moderate device-to-device variation and high yield. They also provide two orders of magnitude higher energy efficiency for multiply-accumulate operations than graphics processing units. We use two 12 x 8 arrays of memristive devices for the in situ training of a 19 x 8 memristive restricted Boltzmann machine for pattern recognition via a gradient descent algorithm based on contrastive divergence. We then create a memristive deep belief neural network consisting of three memristive restricted Boltzmann machines. We test this system using the modified National Institute of Standards and Technology dataset, demonstrating a recognition accuracy of up to 97.05%.