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• W3088098049 abstract "Magnetic skyrmions are emerging as potential candidates for next generation non-volatile memories. In this paper, we propose an in-memory binary neural network (BNN) accelerator based on the non-volatile skyrmionic memory, which we call as SIMBA. SIMBA consumes 26.7 mJ of energy and 2.7 ms of latency when running an inference on a VGG-like BNN. Furthermore, we demonstrate improvements in the performance of SIMBA by optimizing material parameters such as saturation magnetization, anisotropic energy and damping ratio. Finally, we show that the inference accuracy of BNNs is robust against the possible stochastic behavior of SIMBA (88.5% +/- 1%)." @default.
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• W3088098049 date "2020-11-01" @default.
• W3088098049 modified "2023-10-14" @default.
• W3088098049 title "SIMBA: A Skyrmionic In-Memory Binary Neural Network Accelerator" @default.
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• W3088098049 doi "https://doi.org/10.1109/tmag.2020.3024172" @default.
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