Modeling FPGA-Based Systems via Few-Shot Learning

Gagandeep Singh, Dionysios Diamantopolous, Juan Gómez-Luna, S. Stuijk, O. Mutlu, H. Corporaal
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引用次数: 4

Abstract

Machine-learning-based models have recently gained traction as a way to overcome the slow downstream implementation process of FPGAs by building models that provide fast and accurate performance predictions. However, these models suffer from two main limitations: (1) a model trained for a specific environment cannot predict for a new, unknown environment; (2) training requires large amounts of data (features extracted from FPGA synthesis and implementation reports), which is cost-inefficient because of the time-consuming FPGA design cycle. In various systems (e.g., cloud systems), where getting access to platforms is typically costly, error-prone, and sometimes infeasible, collecting enough data is even more difficult. Our research aims to answer the following question: for an FPGA-based system, can we leverage and transfer our ML-based performance models trained on a low-end local system to a new, unknown, high-end FPGA-based system, thereby avoiding the aforementioned two main limitations of traditional ML-based approaches? To this end, we propose a transfer-learning-based approach for FPGA-based systems that adapts an existing ML-based model to a new, unknown environment to provide fast and accurate performance and resource utilization predictions.
通过Few-Shot学习建模fpga系统
基于机器学习的模型最近获得了牵引力,通过构建提供快速准确性能预测的模型来克服fpga缓慢的下游实现过程。然而,这些模型有两个主要的局限性:(1)针对特定环境训练的模型不能预测新的未知环境;(2)训练需要大量的数据(从FPGA合成和实现报告中提取的特征),由于FPGA的设计周期很长,这是低成本的。在各种系统(例如云系统)中,访问平台通常成本高昂,容易出错,有时甚至不可行,因此收集足够的数据更加困难。我们的研究旨在回答以下问题:对于基于fpga的系统,我们能否利用并将在低端本地系统上训练的基于ml的性能模型转移到一个新的、未知的、高端的基于fpga的系统上,从而避免上述传统基于ml的方法的两个主要限制?为此,我们为基于fpga的系统提出了一种基于迁移学习的方法,该方法将现有的基于ml的模型适应于新的未知环境,以提供快速准确的性能和资源利用率预测。
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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