论文标题
实时人工智能应用程序中的任务的基于快速边缘的同步器
A Fast Edge-Based Synchronizer for Tasks in Real-Time Artificial Intelligence Applications
论文作者
论文摘要
在使用可用设备时，实时人工智能（AI）应用程序需要在边缘计算上映射到Edge Computing，以在给定界限内执行数据捕获，过程数据和设备致动。跨设备的任务同步是一个重要的问题，它通过确定捕获的数据的质量，处理数据的时间和驱动质量来影响AI应用程序的及时进展。在本文中，我们开发了一个基于快速的基于边缘的同步方案，该方案可以计算输入输出任务的执行以及计算任务。快速同步器的主要思想是将设备聚集到在其任务执行中高度同步的组，并使用游戏理论求解器静态地确定几乎没有同步点。设备集群使用较晚的通知协议来选择预计的同步点之间的最佳点，以尽快达到时间对齐任务执行。我们使用痕量驱动的模拟评估了同步方案的性能，并将性能与现有的分布式同步方案进行比较，用于实时AI应用程序任务。我们实施同步方案，并将其培训准确性和培训时间与其他参数服务器同步框架进行比较。
Real-time artificial intelligence (AI) applications mapped onto edge computing need to perform data capture, process data, and device actuation within given bounds while using the available devices. Task synchronization across the devices is an important problem that affects the timely progress of an AI application by determining the quality of the captured data, time to process the data, and the quality of actuation. In this paper, we develop a fast edge-based synchronization scheme that can time align the execution of input-output tasks as well compute tasks. The primary idea of the fast synchronizer is to cluster the devices into groups that are highly synchronized in their task executions and statically determine few synchronization points using a game-theoretic solver. The cluster of devices use a late notification protocol to select the best point among the pre-computed synchronization points to reach a time aligned task execution as quickly as possible. We evaluate the performance of our synchronization scheme using trace-driven simulations and we compare the performance with existing distributed synchronization schemes for real-time AI application tasks. We implement our synchronization scheme and compare its training accuracy and training time with other parameter server synchronization frameworks.
