Distributed Inference over Decision Tree Ensembles on Clusters of FPGAs

Muhsen Owaida, Amit Kulkarni, G. Alonso
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引用次数: 11

Abstract

Given the growth in data inputs and application complexity, it is often the case that a single hardware accelerator is not enough to solve a given problem. In particular, the computational demands and I/O of many tasks in machine learning often require a cluster of accelerators to make a relevant difference in performance. In this article, we explore the efficient construction of FPGA clusters using inference over Decision Tree Ensembles as the target application. The article explores several levels of the problem: (1) a lightweight inter-FPGA communication protocol and routing layer to facilitate the communication between the different FPGAs, (2) the data partitioning and distribution strategies maximizing performance, (3) and an in depth analysis on how applications can be efficiently distributed over such a cluster. The experimental analysis shows that the resulting system can support inference over decision tree ensembles at a significantly higher throughput than that achieved by existing systems.
fpga集群上决策树集成的分布式推理
考虑到数据输入和应用程序复杂性的增长,单个硬件加速器往往不足以解决给定的问题。特别是,机器学习中许多任务的计算需求和I/O通常需要一组加速器来产生相关的性能差异。在本文中,我们探索了利用决策树集成推理作为目标应用的FPGA集群的有效构建。本文探讨了几个层次的问题:(1)一个轻量级的fpga间通信协议和路由层,以促进不同fpga之间的通信,(2)数据分区和分布策略最大化的性能,(3)和深入分析如何应用程序可以有效地分布在这样一个集群。实验分析表明,所得到的系统能够以比现有系统更高的吞吐量支持对决策树集成的推理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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