The desire to empower resource-limited edge devices with computer vision (CV) must overcome the high energy consumption of collecting and processing vast sensory data. To address the challenge, this work proposes an energy-efficient non-von-Neumann in-pixel processing solution for neuromorphic vision sensors employing emerging (X) magnetic domain wall magnetic tunnel junction (MDWMTJ) for the first time, in conjunction with CMOS-based neuromorphic pixels. Our hybrid CMOS+X approach performs in-situ massively parallel asynchronous analog convolution, exhibiting low power consumption and high accuracy across various CV applications by leveraging the non-volatility and programmability of the MDWMTJ. Moreover, our developed device-circuit-algorithm co-design framework captures device constraints (low tunnel-magnetoresistance, low dynamic range) and circuit constraints (non-linearity, process variation, area consideration) based on monte-carlo simulations and device parameters utilizing GF22nm FD-SOI technology. Our experimental results suggest we can achieve an average of 45.3% reduction in backend-processor energy, maintaining similar front-end energy compared to the state-of-the-art and high accuracy of 79.17% and 95.99% on the DVS-CIFAR10 and IBM DVS128-Gesture datasets, respectively.

翻译：为资源受限的边缘设备赋予计算机视觉能力须克服海量传感数据采集与处理带来的高能耗挑战。本文首次提出采用新兴磁畴壁运动型磁性隧道结（MDWMTJ）与CMOS神经形态像素相结合的能效型非冯·诺依曼像素内处理方案。该混合CMOS+X架构通过利用MDWMTJ的非易失性与可编程性，实现了原位大规模异步模拟卷积处理，在多种计算机视觉应用中展现出低功耗与高精度优势。基于GF22nm FD-SOI工艺参数与蒙特卡洛仿真，我们开发的器件-电路-算法协同设计框架有效捕获了器件约束（低隧穿磁电阻比、低动态范围）与电路约束（非线性、工艺波动、面积考量）。实验结果表明，与现有最优方案相比，本方法在保持前端能耗基本不变的前提下，后端处理器的能耗平均降低45.3%，在DVS-CIFAR10与IBM DVS128-Gesture数据集上分别达到79.17%与95.99%的高精度。