论文标题
卷积神经网络的SHE-MTJ电路
SHE-MTJ Circuits for Convolutional Neural Networks
论文作者
论文摘要
我们报告了基于先前提供的多重蓄能激活池的概念卷积神经网络的性能特征，这是一种基于MTJ的Spintronic电路，可在并行计算多个神经功能。使用此网络对使用MNIST手写数字数据集进行图像分类的研究是通过仿真提供的。更改重量表示精度，设备过程变化的严重性和计算冗余的效果。仿真网络可实现90至95 \％图像分类的精度，每个图像的成本约为100 nj。
We report the performance characteristics of a notional Convolutional Neural Network based on the previously-proposed Multiply-Accumulate-Activate-Pool set, an MTJ-based spintronic circuit made to compute multiple neural functionalities in parallel. A study of image classification with the MNIST handwritten digits dataset using this network is provided via simulation. The effect of changing the weight representation precision, the severity of device process variation within the MAAP sets and the computational redundancy are provided. The emulated network achieves between 90 and 95\% image classification accuracy at a cost of ~100 nJ per image.
