SBIR Phase I: Efficient Custom Platforms for Smart Computer Vision in the Internet of Things

SBIR 第一阶段：物联网智能计算机视觉的高效定制平台

• 批准号: 1648023
• 负责人: Kyle Rupnow
• 金额: $ 22.5万
• 依托单位: Inspirit IoT, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-01 至 2018-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1648023&HistoricalAwards=false
• 关键词: SBIR; Phase; Efficient; Custom; Platforms

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result in a significant improvement in the performance, power, and cost of deploying smart computer vision applications. This improvement will simplify the deployment of smart vision applications in automotive, sports and entertainment, consumer, robotics and machine vision, medical, and security/surveillance domains. Through a unique and energy-efficient hardware computation platform, automated hardware design for machine learning applications, and efficient implementation libraries, this project will improve both the feature set and efficiency of smart vision applications across a wide range of end-use cases. With the rapid growth in smart vision applications, this project will be a key enabling technology to support high-performance, energy efficient and scalable solutions. Internet of Things (IoT) applications promise to produce billions in revenue and trillions in global economic impact through improved efficiency, safety, energy, and labor costs. Wide deployment of customized computing in IoT applications will lead to substantial energy savings, and a corresponding reduction in carbon emissions, and a more sustainable growth model for deployment of intelligent sensor systems with thousands or millions of sensor nodes analyzing large volumes of input data.The proposed project focuses on the design of high performance, energy-efficient IoT computer vision platforms, design tools, and implementation libraries. Field-programmable gate arrays (FPGAs) are an attractive design and implementation platform to meet performance and energy goals; however, there are two main challenges to their adoption (1) small FPGAs are cost-effective but insufficient to replace the efficient, low-cost ASICs for computation-demanding applications; large FPGAs can fulfill all computation demands, but are too expensive to meet IoT price points, and (2) design and development for FPGAs is challenging and require hardware design expertise. This project?s innovation targets these challenges. First, our proposed platform will combine a media ASIC for efficient video processing with a small cost-effective FPGA for custom machine learning. Second, our proposed domain specific high level synthesis will generate efficient machine learning accelerators for standard machine learning infrastructures quickly, limiting required hardware design expertise while out-performing general purpose design techniques. This project will leverage background expertise in hardware design, design tools, and machine learning to develop and demonstrate the advantages of hybrid computation platforms for smart vision applications in terms of performance, energy consumption, cost and physical size.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将导致部署智能计算机视觉应用的性能，功耗和成本的显着改善。这一改进将简化智能视觉应用在汽车、体育和娱乐、消费、机器人和机器视觉、医疗以及安全/监控领域的部署。通过独特的高能效硬件计算平台、机器学习应用的自动化硬件设计以及高效的实现库，该项目将在广泛的最终用例中提高智能视觉应用的功能集和效率。随着智能视觉应用的快速增长，该项目将成为支持高性能、高能效和可扩展解决方案的关键技术。物联网（IoT）应用有望通过提高效率、安全性、能源和劳动力成本来创造数十亿美元的收入和数万亿美元的全球经济影响。物联网应用中定制计算的广泛部署将带来大量的能源节约，并相应减少碳排放，以及部署具有数千或数百万传感器节点的智能传感器系统以分析大量输入数据的更可持续的增长模式。拟议项目侧重于设计高性能，节能的物联网计算机视觉平台，设计工具，和实现库。现场可编程门阵列（FPGA）是满足性能和能量目标的有吸引力的设计和实现平台;然而，它们的采用存在两个主要挑战：（1）小型FPGA具有成本效益，但不足以取代高效、低成本的ASIC用于要求计算的应用;大型FPGA可以满足所有计算需求，但过于昂贵，无法满足物联网的价格点，（2）FPGA的设计和开发具有挑战性，需要硬件设计专业知识。这个项目？的创新针对这些挑战。首先，我们提出的平台将结合联合收割机的媒体ASIC的高效视频处理与一个小的成本效益的FPGA定制机器学习。其次，我们提出的特定领域的高级综合将快速为标准机器学习基础设施生成高效的机器学习加速器，限制所需的硬件设计专业知识，同时优于通用设计技术。该项目将利用硬件设计、设计工具和机器学习方面的背景专业知识，开发和展示智能视觉应用的混合计算平台在性能、能耗、成本和物理尺寸方面的优势。