论文标题
基于人工神经网络（ANN）处理器中边缘检测的热力学成本
Thermodynamic Cost of Edge Detection in Artificial Neural Network(ANN)-Based Processors
论文作者
论文摘要
基于架构的散热分析使我们能够揭示给定处理器中效率低下的基本来源，从而为我们提供了路线图，以设计较少的耗散计算方案，独立于实施它们的技术基础。在这项工作中，我们研究了基于人工神经网络（ANN）的处理器中对能量耗散的建筑水平贡献，该处理器经过训练以执行边缘检测任务。我们将ANN的培训和信息处理成本与使用64个像素二进制图像的培训和信息处理成本与传统体系结构和算法的成本进行了比较。我们的结果揭示了基于冯·诺伊曼（Von Neumann）体系结构的通用处理器而训练特定任务的ANN网络的固有效率优势。我们还将提出的性能改进与细胞阵列处理器（CAP）的性能进行了比较，并说明了特殊用途处理器的耗散量的减少。最后，我们根据输入数据结构计算耗散的变化，并显示随机性对信息处理的能量成本的影响。我们获得的结果为一系列处理器的基于任务的基本能量效率分析提供了比较的基础，因此有助于研究处理器的体系结构级描述以及基于计算物理学的热力学成本计算。
Architecture-based heat dissipation analyses allow us to reveal fundamental sources of inefficiency in a given processor and thereby provide us with road-maps to design less dissipative computing schemes independent of technology-base used to implement them. In this work, we study architectural-level contributions to energy dissipation in an Artificial Neural Network (ANN)-based processor that is trained to perform edge-detection task. We compare the training and information processing cost of ANN to that of conventional architectures and algorithms using 64-pixel binary image. Our results reveal the inherent efficiency advantages of an ANN network trained for specific tasks over general-purpose processors based on von Neumann architecture. We also compare the proposed performance improvements to that of Cellular Array Processors (CAPs) and illustrate the reduction in dissipation for special purpose processors. Lastly, we calculate the change in dissipation as a result of input data structure and show the effect of randomness on energetic cost of information processing. The results we obtained provide a basis for comparison for task-based fundamental energy efficiency analyses for a range of processors and therefore contribute to the study of architecture-level descriptions of processors and thermodynamic cost calculations based on physics of computation.
