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• W4200548960 abstract "In this work we devise and train a RRAM-based low-precision neural network with binary weights and 4-bits activations. Full-circuit simulations including the analog neuron peripheral circuitry are run in different conditions, including the effect of RRAM devices nonidealities, to evaluate the reliability and performance of the network when executing a classification task. Results show that the power-throughput trade-off during inference is governed by the neuron circuitry, and that the reset conditions can be tuned to simultaneously maximize energy efficiency and accuracy leading to improved network reliability. Accuracy losses are found to be dominated by the variability of the RRAMs in low resistive state (LRS), which suggests specific strategies for accuracy loss minimization. The network shows excellent performance in terms of accuracy, throughput, and energy efficiency, with robustness to RRAM non-idealities." @default.
• W4200548960 created "2021-12-31" @default.
• W4200548960 creator A5005663559 @default.
• W4200548960 creator A5021479723 @default.
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• W4200548960 date "2021-10-04" @default.
• W4200548960 modified "2023-09-23" @default.
• W4200548960 title "Performances and Trade-offs of Low-Bit Precision Neural Networks based on Resistive Memories" @default.
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• W4200548960 doi "https://doi.org/10.1109/iirw53245.2021.9635626" @default.
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