A Winograd-Based Integrated Photonics Accelerator for Convolutional Neural Networks
A Winograd-Based Integrated Photonics Accelerator for Convolutional Neural Networks
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DOI：
10.1109/jstqe.2019.2957443
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发表时间：
2020-01-01
影响因子：
4.9
通讯作者：
El-Ghazawi, Tarek
中科院分区：
文献类型：
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作者：
Mehrabian, Armin;Miscuglio, Mario;El-Ghazawi, Tarek
在过去的十年里，神经网络（NN）已经成为人工智能（AI）复兴的主流技术。在不同类型的神经网络中，卷积神经网络（CNN）已被广泛采用，因为它们在计算机视觉和语音识别等许多领域取得了领先的成果。这一成功部分是由于广泛可用的有能力的底层硬件平台。与此同时，硬件专业化可以让我们接触到新颖的架构解决方案，这些解决方案可以在手头的任务上胜过通用计算机。虽然不同的应用需要不同的性能指标，但它们都将速度和能效作为高度优先事项。与此同时，光子学处理由于其继承的高速和低功耗特性而重新兴起。在这里，我们通过提出一种基于Winograd滤波算法的CNN加速器设计来研究在CNN中使用光子学的潜力。我们的评估结果表明，虽然光子加速器可以在速度和功率方面与当前最先进的电子平台竞争，但它有可能将能源效率提高三个数量级。
Neural Networks (NNs) have become the mainstream technology in the artificial intelligence (AI) renaissance over the past decade. Among different types of neural networks, convolutional neural networks (CNNs) have been widely adopted as they have achieved leading results in many fields such as computer vision and speech recognition. This success in part is due to the widespread availability of capable underlying hardware platforms. In parallel, hardware specialization can expose us to novel architectural solutions, which can outperform general purpose computers for the tasks at hand. Although different applications demand for different performance measures, they all share speed and energy efficiency as high priorities. Meanwhile, photonics processing has seen a resurgence due to its inherited high speed and low power nature. Here, we investigate the potential of using photonics in CNNs by proposing a CNN accelerator design based on Winograd filtering algorithm. Our evaluation results show that while a photonic accelerator can compete with current state-of-the-art electronic platforms in terms of both speed and power, it has the potential to improve the energy efficiency by up to three orders of magnitude.