STTR Phase I: Rebooting Artificial Intelligence Inference with a New Configurable Computing Fabric

STTR 第一阶段：使用新的可配置计算结构重启人工智能推理

• 批准号: 2036249
• 负责人: Arif Iqbal
• 金额: $ 25.6万
• 依托单位: CROSSTALK LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2023-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036249&HistoricalAwards=false
• 关键词: STTR; Phase; Rebooting; Artificial; Intelligence

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve data harnessing and real-time intelligent decision making. The proposed technology is a new reconfigurable computing platform capable of performing a variety of Artificial Intelligence (AI) tasks in a distributed and parallel manner to deliver the best performance at a lower cost. The end products are AI accelerator chips that can be integrated in Accelerator Cards or as co-processors to be applied in both server and edge computing solutions to accelerate AI tasks. The general market need is particularly acute for data center and cloud computing industries where major pain points are performance bottlenecks and high costs due to custom chips or reliance on graphic processing units. The core value propositions of the proposed technology are faster compute, programmability at run-time, and easier integration with existing software to enable execution of popular machine learning frameworks.This Small Business Innovation Research Phase (SBIR) Phase I project centers around a novel computing approach where computing and memory elements are parallel and distributed, and interconnection between computing elements is flexible. The project develops an integrated circuit chip that can be reconfigured at run-time to behave as a custom application-specific integrated circuit for each running Artificial Intelligence (AI) application to deliver the optimal performance. It will overcome the memory bottleneck that traditional computing technologies face where data needs to be continuously loaded to and from memory. The proposed technology also addresses the adaptivity challenge for evolving AI models and datasets. The proposed activities include a chip fabrication using a 28nm commercial semiconductor foundry process, extensive benchmarking of the new technology for scalability, adaptability to data size and shape, and research on a software interface to execute codes developed by existing machine learning frameworks in the new chip. The prototype chip is expected to demonstrate distributed and parallel computing capabilities along with dynamic reconfigurability. The benchmarking work is anticipated to reveal at least an order of magnitude improvement over more conventional graphics processing unit based approaches.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响/商业潜力是改善利用数据和实时智能决策。提出的技术是一个新的可重构计算平台，能够以分布式和并行的方式执行各种人工智能（AI）任务，以较低的成本提供最佳性能。最终产品是AI加速器芯片，可以集成在加速器卡中，也可以作为将在服务器和边缘计算解决方案中应用于加速AI任务的协作处理器。对于数据中心和云计算行业来说，一般市场需求特别急切，在这些行业中，主要的疼痛点是性能瓶颈和由于定制芯片或依赖图形处理单元而导致的高成本。所提出的技术的核心价值主张是更快的计算，运行时的可编程性以及与现有软件更容易集成，以实现流行的机器学习框架。这本小型企业创新研究阶段（SBIR）I阶段项目中心围绕一种新颖的计算方法，其中计算和内存元素是并行和分布，并且在计算元素之间是平行和分布的，并且计算元素之间的互联元素是灵活的。该项目开发了一个集成的电路芯片，可以在运行时重新配置，以作为每个运行的人工智能（AI）应用程序的自定义应用程序集成电路的行为，以提供最佳性能。它将克服传统计算技术所面临的内存瓶颈，其中需要连续加载数据和从内存中加载数据。 提出的技术还解决了不断发展的AI模型和数据集的适应性挑战。拟议的活动包括使用28nm商业半导体铸造工艺进行芯片制造，新技术的广泛基准测试，对数据大小和形状的适应性以及对软件界面进行研究以执行新芯片中现有机器学习框架开发的代码。原型芯片有望证明分布式和并行计算功能以及动态重构性。 预计基准测试的工作将至少揭示基于更常规的图形处理单元方法的数量级改进。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子和更广泛的影响审查标准，被认为值得通过评估。