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

这个小企业创新研究（SBIR）第一阶段项目的广泛影响/商业潜力将导致部署智能计算机视觉应用程序的性能，功率和成本的显着改善。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、医疗和安全/监控领域的部署。通过独特且节能的硬件计算平台，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、节能和可扩展解决方案的关键使能技术。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，创造数十亿美元的收入和数万亿美元的全球经济影响。在物联网应用中广泛部署定制计算将节省大量能源，并相应减少碳排放，并为部署具有数千或数百万传感器节点分析大量输入数据的智能传感器系统提供更可持续的增长模式。本项目重点设计高性能、高能效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）是一个有吸引力的设计和实现平台，以满足性能和能源目标；然而，它们的采用面临两个主要挑战：(1)小型fpga具有成本效益，但不足以取代高效，低成本的asic，用于计算要求高的应用；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，并且(2)fpga的设计和开发具有挑战性，需要硬件设计专业知识。这个项目吗?美国的创新正是针对这些挑战。首先，我们提出的平台将结合用于高效视频处理的媒体ASIC和用于定制机器学习的小型经济高效FPGA。其次，我们提出的特定领域的高级综合将为标准机器学习基础设施快速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。

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.