XPS: FULL: DSD: Collaborative Research: FPGA Cloud Platform for Deep Learning, Applications in Computer Vision

XPS：完整：DSD：协作研究：深度学习 FPGA 云平台、计算机视觉应用

• 批准号: 1533771
• 负责人: Alexander Berg
• 金额: $ 30.1万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533771&HistoricalAwards=false
• 关键词: XPS; FULL; DSD; Collaborative; Research

We stand on the verge of dramatic advances in deep learning applications, which will soon enable practicality and widespread adoption of computer vision based recognition in scientific inquiry, commercial applications, and everyday life. Grand challenge problems are within our reach; we will soon be able to build automated systems that recognize nearly everything we see, systems that can recognize the tens of thousands of basic-level categories that psychologists posit humans can recognize, systems that continuously learn from photos, video, and web content in order to create more complete and accurate visual models of the world. However, while it is clear that the computational capabilities for deep learning are within reach, it is equally clear that the required computational power cannot come from general-purpose processors. To succeed, we will need to build specialized domain-specific computing systems based on hardware accelerators that are capable of exploiting the extreme fine-grained parallelism inherent in deep-learning workloads. This project leverages parallelization and reconfigurable hardware to create an automated system that distributes computer vision algorithms onto a large number of field-programmable gate arrays (FPGA Cloud). This project builds on recent advances in domain-specific hardware generation tools in order to bring the potential parallelism and performance per watt advantages of FPGAs to large-scale computer vision problems. By developing a platform to run deep learning algorithms on large clouds of FPGAs, this proposal explicitly addresses scaling algorithms beyond what a single chip can process. This involves addressing a wide range of challenging problems in algorithm analysis, building domain-specific hardware generators, communication for scaling algorithms across multiple FPGAs, and extensive validation of generating hardware for state-of-the-art deep learning approaches applied to computer vision problems. This project advances tools for designing domain-specific FPGA implementations of algorithms, taking a step toward making more efficient computing with greater parallelism more widely available. In particular, for computer vision, there will be significant benefits from a product of multiple improvements: higher parallelism, lower gate requirement by moving to fixed point when possible, and better performance per watt leading to higher computation density in servers. Together, these have the potential to significantly increase the extent to which computer vision can be a part of our daily lives, making computers better able to understand the context of our world.

我们站在深度学习应用中的巨大进步的边缘，这将很快实现实用性，并广泛采用基于计算机的科学探究，商业应用和日常生活中的识别。 巨大的挑战问题在我们的范围内；我们很快将能够构建自动化系统，这些系统几乎可以识别我们看到的所有内容，这些系统可以识别出心理学家认为人类可以识别的成千上万基本级别类别，这些系统可以从照片，视频和网络内容中不断学习，以创建更完整的世界视觉模型。 但是，虽然很明显，深度学习的计算能力已触及，但同样清楚的是，所需的计算能力不能来自通用处理器。 为了取得成功，我们将需要基于能够利用深度学习工作负载固有的极端细粒度并行性的硬件加速器来构建专业领域的计算系统。 该项目利用并行化和可重新配置的硬件创建一个自动化系统，该系统将计算机视觉算法分布到大量现场可编程的门阵列（FPGA云）上。该项目以特定于域的硬件生成工具的最新进展为基础，以便将FPGA的潜在平行性和性能带到大规模的计算机视觉问题上。 通过开发一个平台来在大型FPGA云上运行深度学习算法，该建议明确地解决了扩展算法超出单个芯片可以处理的算法。 这涉及在算法分析中解决广泛的挑战性问题，构建域特异性硬件生成器，用于扩展多个FPGA的算法的通信，以及对用于计算机视觉问题的最先进深度学习方法生成硬件的广泛验证。 该项目推进了设计算法的特定领域FPGA实现的工具，并迈出了更有效的计算，更广泛地可用。 特别是，对于计算机视觉，多种改进的产物将带来显着的好处：高等的并行性，在可能的情况下移动到固定点的较低门需求以及每瓦更高的性能，从而导致服务器中更高的计算密度。 共同，这些潜力可以显着增加计算机视觉成为我们日常生活的一部分的程度，从而使计算机能够更好地了解我们的世界背景。