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- W4309100305 abstract "In spite of recent advancements in artificial neural networks (ANNs), the energy efficiency, multifunctionality, adaptability, and integrated nature of biological neural networks remain largely unimitated by 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, multipixel, and “all-in-one” bioinspired neural network (BNN) capable of sensing, encoding, learning, forgetting, and inferring at minuscule energy expenditure. We also demonstrate learning adaptability and simulate 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 to not only overcome the bottleneck of von Neumann computing in conventional CMOS designs but also to aid in eliminating the peripheral components necessary for competing technologies such as memristors." @default.
- W4309100305 created "2022-11-22" @default.
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- W4309100305 date "2022-11-15" @default.
- W4309100305 modified "2023-09-29" @default.
- W4309100305 title "An All-in-One Bioinspired Neural Network" @default.
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- W4309100305 doi "https://doi.org/10.1021/acsnano.2c02172" @default.
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