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- W4226006768 abstract "In this work, we discussed the critical challenges, key enabling techniques, and future trends on developing RRAM-based brain-inspired computation, including computing-in-memory (CIM) and neuromorphic computing (NC), from device, circuit to system. To suppress the device non-idealities in the synaptic array, we proposed using optimized bit-cell design and computing approach to reduce the errors and power of the analogue multiply-and-accumulate (MAC). To lower the neuron power, we proposed the sparsity-aware analog-to-digital converter (ADC) for artificial neural networks (ANNs) and highlighted the energy- and area-efficient bio-plausible neurons based on NbO <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>x</inf> devices for spiking neural networks (SNNs). On this basis, we introduce several RRAM CIM designs, followed by a discussion on the remaining challenges and future trends." @default.
- W4226006768 created "2022-05-05" @default.
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- W4226006768 date "2021-12-11" @default.
- W4226006768 modified "2023-10-17" @default.
- W4226006768 title "Enabling RRAM-Based Brain-Inspired Computation by Co-design of Device, Circuit, and System" @default.
- W4226006768 doi "https://doi.org/10.1109/iedm19574.2021.9720546" @default.
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