论文标题
Litecon：能节能深度学习（预印本）的全球神经形态加速器
LiteCON: An All-Photonic Neuromorphic Accelerator for Energy-efficient Deep Learning (Preprint)
论文作者
论文摘要
在当今的数据密集型时代，深度学习非常普遍。特别是，卷积神经网络（CNN）在各种领域被广泛采用，以提高准确性。但是，计算传统CPU和GPU的深入CNN带来了几种性能和能量陷阱。最近已经证明了基于ASIC，FPGA和电阻内存设备的几种新型方法，并有令人鼓舞的结果。他们中的大多数仅针对深度学习的推论（测试）阶段。尝试设计能够培训和推理的成熟深度学习加速器的尝试非常有限。这是由于训练阶段的高度计算和记忆密集型性质。在本文中，我们提出了一种新型的模拟光子CNN加速器Litecon。 Litecon使用基于硅微电极的卷积，基于Memristor的内存以及密集的波长 - 划分型 - 能节能和超快深度学习。我们使用商业CAD框架（ipkiss）在包括Lenet和VGG-NET在内的深度学习基准模型上评估Litecon。与最先进的情况相比，LiteCon分别以微不足道的精度降解，将CNN的吞吐量，能源效率和计算效率分别提高了32倍，37倍和5倍。
Deep learning is highly pervasive in today's data-intensive era. In particular, convolutional neural networks (CNNs) are being widely adopted in a variety of fields for superior accuracy. However, computing deep CNNs on traditional CPUs and GPUs brings several performance and energy pitfalls. Several novel approaches based on ASIC, FPGA, and resistive-memory devices have been recently demonstrated with promising results. Most of them target only the inference (testing) phase of deep learning. There have been very limited attempts to design a full-fledged deep learning accelerator capable of both training and inference. It is due to the highly compute and memory-intensive nature of the training phase. In this paper, we propose LiteCON, a novel analog photonics CNN accelerator. LiteCON uses silicon microdisk-based convolution, memristor-based memory, and dense-wavelength-division-multiplexing for energy-efficient and ultrafast deep learning. We evaluate LiteCON using a commercial CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. Compared to the state-of-the-art, LiteCON improves the CNN throughput, energy efficiency, and computational efficiency by up to 32x, 37x, and 5x respectively with trivial accuracy degradation.
