论文标题
Simba：天空中的内存二进制神经网络加速器
SIMBA: A Skyrmionic In-Memory Binary Neural Network Accelerator
论文作者
论文摘要
磁性天空源是下一代非易失性记忆的潜在候选者。在本文中，我们提出了基于非挥发性存储器的内存二进制神经网络（BNN）加速器，我们称之为Simba。在对类似VGG的BNN进行推断时，Simba消耗了26.7 MJ的能量和2.7毫秒的延迟。此外，我们通过优化材料参数（例如饱和磁化，各向异性能量和阻尼比）来证明SIMBA的性能提高。最后，我们表明，BNN的推理准确性与Simba可能的随机行为（88.5％+/- 1％）具有鲁棒性。
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%).
