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- W3092506270 abstract "Resistive RAM (ReRAM) is a promising device to realize the Computing in Memory (CiM) architecture, suitable for power-constrained IoT systems. Because of low leakage, the dot-production operations in ReRAM crossbars dominate the chip power, especially when implementing low-precision neural networks. This work investigates the correlation between the cell resistance state and the crossbar operation power, and proposes a State-Aware ReRAM Accelerator (SARA) architecture for energy-efficient neural networks. With the proposed state-aware network training and mapping strategy, crossbars in the ReRAM accelerator can perform in a lower-power state. The evaluation shows that our design reduces 47% energy over the baseline without compromising the network accuracy." @default.
- W3092506270 created "2020-10-15" @default.
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- W3092506270 date "2020-07-01" @default.
- W3092506270 modified "2023-09-22" @default.
- W3092506270 title "Towards State-Aware Computation in ReRAM Neural Networks" @default.
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- W3092506270 doi "https://doi.org/10.1109/dac18072.2020.9218729" @default.
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