Magnetic skyrmions are emerging as potential candidates for next-generation non-volatile memories. In this article, we propose an in-memory binary neural network (BNN) accelerator based on the non-volatile skyrmionic memory, which we call as Skyrmionic In-Memory BNN Accelerator (SIMBA). SIMBA consumes 26.7 mJ of energy and 2.7 ms of latency when running inference on a VGG-like BNN. In addition, SIMBA saves up to 97.07% in energy consumption with $3.73\times $ speedup compared with the other accelerators in the literature at similar inference accuracy. 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%).

中文翻译：

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SIMBA：Skyrmionic 内存中二进制神经网络加速器

磁性斯格明子正在成为下一代非易失性存储器的潜在候选者。在本文中，我们提出了一种基于非易失性 Skyrmionic 存储器的内存中二进制神经网络 (BNN) 加速器，我们将其称为 Skyrmionic In-Memory BNN Accelerator (SIMBA)。在类似 VGG 的 BNN 上运行推理时，SIMBA 消耗 26.7 mJ 的能量和 2.7 ms 的延迟。此外，在类似推理精度的情况下，与文献中的其他加速器相比，SIMBA 可节省高达 97.07% 的能耗，加速 3.73 美元。此外，我们通过优化材料参数（例如饱和磁化强度、各向异性能量和阻尼比）证明了 SIMBA 性能的改进。最后，我们证明了 BNN 的推理精度对于 SIMBA 可能的随机行为是稳健的（88.5%±1%）。