发表机构
Aalborg University（奥尔堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基础设施原生计算，利用电网物理定律作为计算算子，通过仿真验证其在图像分类任务中达到高准确率，并指出拓扑、控制通道和输入表示决定其效用。
AI 中文摘要
计算通常由为信息处理而设计的硬件实现。在此，我们研究基础设施原生计算：将为其他主要功能而构建的物理系统用作固定计算算子。在IEEE 14节点电网的时域仿真中，基尔霍夫电流定律和欧姆定律将通过电力电子变换器接口的分布式可控节点上施加的电压参考扰动，与电流响应通过依赖于拓扑的变换联系起来。一个经过训练的数字化编码器和解码器利用此变换进行图像分类，在MNIST上达到91.5%的准确率，在Fashion-MNIST上达到82.25%。所建模的算子由933个替代参数表示，而与之准确度匹配的全连接核心变换则需要12,340个任务训练参数。电流叠加进一步支持算子的并发空间共享和顺序时间复用，在替代模型评估中，每流准确率分别高于85%和93%。在CIFAR-10以及从蝴蝶与蛾数据集中采样的重复10类任务上的评估表明，物理算子的增量效用取决于上游数字特征提取所提供的表示。这些结果为电网基础设施原生计算提供了基于仿真的概念验证，并确定了拓扑、可访问的控制通道和输入表示是其计算效用的决定因素。
英文摘要
Computing is conventionally implemented by hardware engineered for information processing. Here we investigate infrastructure-native computing: the use of a physical system built for another primary function as a fixed computational operator. In time-domain simulations of an IEEE 14-bus electrical network, Kirchhoff's current law and Ohm's law relate voltage-reference perturbations applied at distributed controllable nodes interfaced by power electronics converters to current responses through a topology-dependent transformation. A trained digital encoder and decoder exploit this transformation for image classification, reaching 91.5% accuracy on MNIST and 82.25% on Fashion-MNIST. The modeled operator is represented by 933 surrogate parameters, compared with 12,340 task-trained parameters for an accuracy-matched fully connected core transformation. Current superposition further supports concurrent spatial sharing of the operator and sequential temporal reuse, with per-stream accuracies above 85% and 93%, respectively, in surrogate-model evaluations. Evaluations on CIFAR-10 and repeated 10-class tasks sampled from a Butterflies-and-Moths dataset show that the incremental utility of the physical operator depends on the representation supplied by upstream digital feature extraction. These results provide a simulation-based proof of concept for infrastructure-native computing with electrical networks and identify topology, accessible control channels, and input representation as determinants of its computational utility.
