Preliminary Study to Demonstrate the Performance and Power Advantages of FPGAs over GPUs for Deep Learning in Computer Vision

我们即将在深度学习算法方面取得巨大进步，这将很快使基于计算机视觉的对象识别在科学研究、商业应用和日常生活中得到广泛采用。然而，目前这一领域的实际大规模应用受到传统计算机系统计算能力的限制。近年来，计算机处理器(CPU)的技术进步已经相当缓慢。这导致了人们对使用图形处理单元(GPU)来加速深度学习计算机视觉算法的兴趣的增加。虽然GPU可以比CPU更快地执行这些任务，但它们存在灵活性不高和非常高的功率成本的问题。另一种技术称为现场可编程门阵列(现场可编程门阵列)，由于其灵活性和功率效率，对于这一领域的问题非常有吸引力。然而，在这一领域，现场可编程门阵列一直未得到充分利用，这在很大程度上是由于不熟悉和误解。这个项目的目标是通过确凿的实验证据来展示在基于深度学习的计算机视觉问题上，现场可编程门阵列相对于图形处理器的能力和性能优势。这项计划包括三个阶段的研究计划，目的是鼓励在解决深度学习和电脑视野的重大挑战时，使用现场可编程门阵列进行开创性工作。首先，PI将准备并验证基于卷积神经网络的最先进的图像检测应用程序。这将利用流行的Caffe库，该库允许在CPU和GPU上评估卷积网络。其次，PI将在GPU上对该应用程序的性能进行详细的表征和剖析，试图了解性能特征及其潜在原因。第三，PI将在FPGA上实现部分算法，并进行深入分析，以找到并解释该平台提供的优势和劣势。PI预计，由于对不规则细粒度并行的有效支持，GPU上最慢的算法部分将在FPGA上实现显著的加速。同时，算法在GPU上的最快部分预计将在FPGA上运行，性能与之相当，但功耗大大降低。该项目将把研究与研究生和本科教育结合起来。博士生将接触到GPU优化和特定于应用程序的高性能FPGA设计。硕士和本科生将通过计算机科学硕士高级项目和电气和计算机工程本科高级设计项目获得帮助项目的宝贵技能。研究结果将在显眼的地点公布，以确保相关研究界获得最大程度的曝光率。

We stand on the verge of dramatic advances in deep learning algorithms, which will soon enable widespread adoption of computer-vision-based object recognition in scientific inquiry, commercial applications, and everyday life. However, practical large-scale applications in this area are currently limited by the computational capabilities of conventional computer systems. In recent years, technological improvement in computer processors (CPUs) has considerably slowed. This has led to an increase in interest in using Graphics Processing Units (GPUs) to accelerate deep learning computer vision algorithms. Although GPUs can perform these tasks faster than CPUs, they suffer from inflexibility and very high power cost. An alternative technology called the Field-Programmable Gate Array (FPGA) is very attractive for problems in this domain thanks to its flexibility and power efficiency. However, FPGAs have been underutilized in this area, in large part due to unfamiliarity and misconceptions. The goal of this project is to demonstrate the power and performance advantages of FPGAs over GPUs for deep-learning-based computer vision problems via hard experimental evidence. The PIs will disseminate their findings to the research community at large with the goal of encouraging the use of FPGAs in ground-breaking work tackling the grand challenges of deep learning and computer vision.This project consists of a three-stage research plan. First, the PIs will prepare and validate a state-of-the-art image detection application based on convolutional neural networks. This will utilize the popular Caffe library, which allows convolutional networks to be evaluated on CPU and GPU. Second, the PIs will perform a detailed characterization and profiling of the performance of this application on GPU, seeking to understand the performance characteristics and their underlying causes. Third, the PIs will implement portions of the algorithm on an FPGA, and perform an in-depth analysis to find and explain the advantages and disadvantages offered by the platform. The PIs anticipate demonstrating that the slowest portion of the algorithm on the GPU will achieve significant speedup on the FPGA, arising from the efficient support of irregular fine-grain parallelism. Meanwhile, the fastest portion of the algorithm on the GPU is anticipated to run with comparable performance on the FPGA, but at dramatically lower power consumption.This project will integrate research with graduate and undergraduate education. PhD students will be exposed to GPU optimization and application-specific high-performance FPGA design. Masters and undergraduate students will gain valuable skills assisting the project through the Masters Advanced Project in Computer Science and the Undergraduate Senior Design Project in Electrical and Computer Engineering. The results of the study will be published at prominent venues to ensure maximum exposure for the relevant research communities.