一种用于边缘计算神经形态硬件部署的磁性隧道结设计
A design of magnetic tunnel junctions for the deployment of neuromorphic hardware for edge computing
AI总结：
本文提出一种具有自由层和双极化层的磁性隧道结设计，可在单一器件内实现sigmoid激活函数及其梯度的前向与反向传播计算，实验表明其对器件差异和噪声鲁棒，在Fashion MNIST和CIFAR-100上精度损失分别仅为0.1%和2%，有效支持边缘计算中的迁移学习。
摘要：
鲁棒且可扩展的磁性隧道结（MTJs）具有电可读的复杂动力学特性，为推进神经形态计算提供了广阔前景。在本工作中，我们提出了一种具有自由层和两个极化层的MTJ设计，能够在器件层面计算sigmoid激活函数及其梯度。该设计使得前向传播和反向传播计算可以在单一器件内完成，通过引入直接在硬件中执行反向传播的能力，扩展了文献中先前探索的神经形态计算框架。我们的算法实现揭示了三项关键发现：（i）MTJ生成的曲线与软件生成的精确曲线之间的微小差异对反向传播算法的性能影响可以忽略不计；（ii）该器件实现对于器件间差异和噪声具有高度鲁棒性；（iii）所提出的方法有效支持迁移学习和知识蒸馏。为验证这一点，我们评估了一个边缘计算网络的性能，该网络使用由软件训练的模型权重并通过我们的MTJ设计实现。结果表明，与原始软件实现相比，Fashion MNIST数据集的精度损失仅为0.1%，CIFAR-100数据集的精度损失为2%。这些结果凸显了我们的MTJ设计在边缘计算应用中用于紧凑型硬件神经网络的潜力，尤其是在迁移学习方面。
英文摘要：
The electrically readable complex dynamics of robust and scalable magnetic tunnel junctions (MTJs) offer promising opportunities for advancing neuromorphic computing. In this work, we present an MTJ design with a free layer and two polarizers capable of computing the sigmoidal activation function and its gradient at the device level. This design enables both feedforward and backpropagation computations within a single device, extending neuromorphic computing frameworks previously explored in the literature by introducing the ability to perform backpropagation directly in hardware. Our algorithm implementation reveals two key findings: (i) the small discrepancies between the MTJ-generated curves and the exact software-generated curves have a negligible impact on the performance of the backpropagation algorithm, (ii) the device implementation is highly robust to inter-device variation and noise, and (iii) the proposed method effectively supports transfer learning and knowledge distillation. To demonstrate this, we evaluated the performance of an edge computing network using weights from a software-trained model implemented with our MTJ design. The results show a minimal loss of accuracy of only 0.1% for the Fashion MNIST dataset and 2% for the CIFAR-100 dataset compared to the original software implementation. These results highlight the potential of our MTJ design for compact, hardware-based neural networks in edge computing applications, particularly for transfer learning.
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