一种基于自旋电子器件的节能细胞神经网络方案
A Proposal for Energy-Efficient Cellular Neural Network based on Spintronic Devices
摘要：
细胞神经网络（CNN）具有大规模并行计算能力和出色的图像和信号处理性能，是一种很有前途的非布尔计算系统，它可以超越传统的数字逻辑计算，并可以缓解传统CMOS工艺的物理缩放限制。CNN最初是通过VLSI模拟技术实现的，其中运算放大器和运算放大器分别作为神经元和突触，这是功耗和面积消耗。在本文中，我们提出了一种混合结构来实现CNN与磁性元件和CMOS外设与一个完整的驱动和检测电路。此外，我们提出了一个数字可编程的磁突触，可以实现积极和消极的模板值。经过严格的性能分析和比较，最佳的能量是基于各种设计参数，包括驱动电压和CMOS驱动尺寸。在可比较的占地面积和操作速度下，与CMOS对应物相比，自旋电子CNN预计每次操作的能量减少超过一个数量级。
英文摘要：
Due to the massive parallel computing capability and outstanding image and signal processing performance, cellular neural network (CNN) is one promising type of non-Boolean computing system that can outperform the traditional digital logic computation and mitigate the physical scaling limit of the conventional CMOS technology. The CNN was originally implemented by VLSI analog technologies with operational amplifiers and operational transconductance amplifiers as neurons and synapses, respectively, which are power and area consuming. In this paper, we propose a hybrid structure to implement the CNN with magnetic components and CMOS peripherals with a complete driving and sensing circuitry. In addition, we propose a digitally programmable magnetic synapse that can achieve both positive and negative values of the templates. After rigorous performance analyses and comparisons, optimal energy is achieved based on various design parameters, including the driving voltage and the CMOS driving size. At a comparable footprint area and operation speed, a spintronic CNN is projected to achieve more than one order of magnitude energy reduction per operation compared to its CMOS counterpart.
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