Abstract:In-memory computing hardware accelerators for binarized neural networks based on resistive RAM (RRAM) memory technologies represent a promising solution for enabling the execution of deep neural network algorithms on resource-constrained devices at the edge of the network. However, the intrinsic stochasticity and nonidealities of RRAM devices can easily lead to unreliable circuit operations if not appropriately considered during the design phase. In this chapter, analysis and design methodologies enabled by RR… Show more
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