An All-in-One Bio-inspired Neural Network

In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of the biological neural networks largely remain unimitated in hardware neuromorphic computing systems. Here we exploit optoelectronic, computing, and programmable memory devices based on emerging two-dimensional (2D) layered materials such as MoS2 to demonstrate a monolithically integrated, multi-pixel, and “all-in-one” bio-inspired neural network (BNN) capable of sensing, encoding, learning, forgetting, and inferring at miniscule energy expenditure. We also demonstrate learning adaptability and stimulate learning challenges under specific synaptic conditions to mimic biological learning. Our findings highlight the potential of in-memory computing and sensing based on emerging 2D materials, devices, and integrated circuits not only to overcome the bottleneck of von Neumann computing in conventional CMOS designs but also aid in eliminating peripheral components necessary for competing technologies such as memristors.