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.

我们正处于深度学习应用取得巨大进步的边缘，这很快将在科学探究、商业应用和日常生活中实现基于计算机视觉的识别的实用性和广泛采用。 巨大的挑战问题触手可及；我们很快将能够构建能够识别几乎所有我们看到的一切的自动化系统，能够识别心理学家认为人类可以识别的数以万计的基本类别的系统，能够连续地从照片、视频和网络内容中学习的系统创建更完整、更准确的世界视觉模型。 然而，虽然深度学习的计算能力显然是可以实现的，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，我们需要构建基于硬件加速器的专门领域特定计算系统，这些系统能够利用深度学习工作负载中固有的极其细粒度的并行性。 该项目利用并行化和可重构硬件创建一个自动化系统，将计算机视觉算法分发到大量现场可编程门阵列（FPGA 云）上。该项目建立在特定领域硬件生成工具的最新进展的基础上，以便将 FPGA 的潜在并行性和每瓦性能优势应用于大规模计算机视觉问题。 通过开发一个在大型 FPGA 云上运行深度学习算法的平台，该提案明确解决了超出单芯片处理能力的扩展算法问题。 这涉及解决算法分析中的各种挑战性问题、构建特定领域的硬件生成器、跨多个 FPGA 扩展算法的通信，以及对应用于计算机视觉问题的最先进深度学习方法的生成硬件进行广泛验证。 该项目改进了用于设计特定领域的 FPGA 算法实现的工具，朝着更广泛地提供更高效的计算和更大的并行性迈出了一步。 特别是，对于计算机视觉而言，多重改进的产品将带来显着的好处：更高的并行性、通过在可能的情况下移动到定点来降低门要求，以及更好的每瓦性能导致服务器中更高的计算密度。 总之，这些有可能显着提高计算机视觉融入我们日常生活的程度，使计算机能够更好地理解我们世界的背景。