论文标题
分配策略对分布式CNN推断的能源消耗的影响
The Effects of Partitioning Strategies on Energy Consumption in Distributed CNN Inference at The Edge
论文作者
论文摘要
如今，许多使用资源受限的边缘设备（例如，小型移动机器人，小型IoT设备等）使用的许多AI应用程序都需要在边缘的分布式系统上推断卷积神经网络（CNN），这是由于单个边缘设备的有限资源来容纳和执行大型CNN。有四种主要分区策略可用于分区大型CNN模型，并在边缘的多个设备上执行分布式CNN推断。但是，据我们所知，尚未进行研究来研究这四种分区策略如何影响每个边缘设备的能源消耗。这样的研究很重要，因为它将揭示这些分区策略的潜力，以便在部署大型CNN模型以在边缘分布式推理时有效地用于减少人均能量消耗。因此，在本文中，我们调查并比较了使用四个分区策略时，在分布式系统上的边缘中CNN模型推断的每磁性能量消耗。我们的调查和比较的目的是找出哪些分区策略（以及在什么条件下）具有最高的潜力，可以在分布式系统的边缘进行CNN推理时降低每个边缘设备的能量消耗。
Nowadays, many AI applications utilizing resource-constrained edge devices (e.g., small mobile robots, tiny IoT devices, etc.) require Convolutional Neural Network (CNN) inference on a distributed system at the edge due to limited resources of a single edge device to accommodate and execute a large CNN. There are four main partitioning strategies that can be utilized to partition a large CNN model and perform distributed CNN inference on multiple devices at the edge. However, to the best of our knowledge, no research has been conducted to investigate how these four partitioning strategies affect the energy consumption per edge device. Such an investigation is important because it will reveal the potential of these partitioning strategies to be used effectively for reduction of the per-device energy consumption when a large CNN model is deployed for distributed inference at the edge. Therefore, in this paper, we investigate and compare the per-device energy consumption of CNN model inference at the edge on a distributed system when the four partitioning strategies are utilized. The goal of our investigation and comparison is to find out which partitioning strategies (and under what conditions) have the highest potential to decrease the energy consumption per edge device when CNN inference is performed at the edge on a distributed system.
