机器学习的硬件加速

2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) Pub Date : 2017-07-03 DOI:10.1109/ISVLSI.2017.127

Ruizhe Zhao, W. Luk, Xinyu Niu, Huifeng Shi, Haitao Wang

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引用次数: 26

摘要

本文提出了一种基于硬件加速来提高机器学习应用性能的方法。该方法基于为卷积神经网络(CNN)和支持向量机(SVM)设计的参数化架构，以及两者共同的相关设计流程。该方法通过目标检测和卫星数据分析两个案例加以说明。提出了该方法的潜力。

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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Hardware Acceleration for Machine Learning

This paper presents an approach to enhance the performance of machine learning applications based on hardware acceleration. This approach is based on parameterised architectures designed for Convolutional Neural Network (CNN) and Support Vector Machine (SVM), and the associated design flow common to both. This approach is illustrated by two case studies including object detection and satellite data analysis. The potential of the proposed approach is presented.

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2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI)

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