一种将卷积层划分为多个fpga的方法

2020 International SoC Design Conference (ISOCC) Pub Date : 2020-10-21 DOI:10.1109/ISOCC50952.2020.9332929

Kensuke Iizuka, Kohe Ito, Kazuei Hironaka, H. Amano

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引用次数: 0

摘要

我们提出了一种分区方法来提高卷积神经网络(CNN)在一个名为Flow-in-Cloud (FiC)的多fpga系统上的性能，并在FiC上实现了AlexNet的第二层。因此，通过优化的机器学习框架，我们的实现比CPU和GPU稍微节能一些。

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A Method of Partitioning Convolutional Layer to Multiple FPGAs

We propose a partition method to improve the performance of convolutional neural networks (CNN) on a multi-FPGA system called Flow-in-Cloud (FiC) and implement the 2nd layer of AlexNet on FiC. As a result, our implementation is slightly more energy-efficient than the CPU and the GPU with an optimized machine learning framework.

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2020 International SoC Design Conference (ISOCC)

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