EAGER: CortiCore - Exploring the Use of An Automata Processor as an MISD Accelerator

EAGER：CortiCore - 探索使用自动机处理器作为 MISD 加速器

• 批准号: 1451571
• 负责人: Mircea Stan
• 金额: $ 15万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1451571&HistoricalAwards=false
• 关键词: EAGER; CortiCore; Exploring; Use; Automata; EAGER; CortiCore; Exploring; Use; Automata

A novel computational accelerator architecture - the Automata Processor -has recently been introduced by Micron, that extends the computational paradigm of non-deterministic finite automata with important new capabilities. This architecture is particularly well suited for tasks involving pattern matching. Preliminary results suggest speedups as high as 1000X are possible, especially applications that entail combinatorial search, i.e., searching among many possible patterns to find the best match. This project evaluates the suitability of this novel architecture for accelerating combinatorial search, using cortical learning algorithms (i.e., algorithms for machine learning that are inspired by observations and/or theories of how the brain works) as a case study. Until now, cortical learning algorithms have primarily been implemented only in software, which leads to solutions that are slow, large, expensive and power hungry, and thus limits their applicability. In particular, this project initially focuses on accelerating hierarchical temporal memory, a cortical learning algorithm that has recently been shown to be highly effective for analysis and integration of high-data-rate, multi-modal sensor and video data. It embodies many characteristics of a variety of combinatorial search tasks, combining and extending techniques from Bayesian networks, clustering, and decision trees. This project is the first to evaluate the ability of the "enhanced automata" paradigm to accelerate cortical learning algorithms, and one of the first to explore the capabilities of the Automata Processor. In the process of evaluating the best way to accelerate cortical learning algorithms, this project will yield insights into the suitability of the Automata Processor for other artificial intelligence algorithms. It will also lead to development of new algorithms, software libraries, programming guidelines, and a new programming interface, to help speed the mapping of other applications to the Automata Processor and future accelerators. It will also yield techniques to improve the performance, flexibility, and energy efficiency of future accelerators, and new insights into the design and programming of heterogeneous systems with diverse accelerator hardware units. This project has potential to lay the foundations for a novel acceleration framework that enables efficient solutions to a large set of intractable problems, with orders-of-magnitude improvements in performance and energy efficiency, and to guide development of future accelerators. As a consequence of these acceleration capabilities, portable, low-power artificial intelligence solutions could become ubiquitous. This project creates tools that facilitate research and product development involving accelerator-based computing. This project contributes to education and outreach through new course materials and assignments, hands-on research and training opportunities in cutting-edge acceleration paradigms, and new academic-industry collaborations.

Micron最近引入了一种新型的计算加速器体系结构 - 自动机处理器 - 扩展了具有重要新功能的非确定性有限自动机的计算范式。该体系结构特别适合涉及模式匹配的任务。 初步结果表明，速度高达1000x，尤其是需要组合搜索的应用程序，即在许多可能的模式中搜索以找到最佳匹配。 该项目评估了这种新颖的体系结构对加速组合搜索的适用性，使用皮质学习算法（即，作为案例研究的观察和/或理论启发的机器学习算法和/或理论的启发）。到目前为止，皮质学习算法主要仅在软件中实现，这导致了缓慢，大，昂贵且饥饿的解决方案，从而限制了其适用性。特别是，该项目最初着重于加速分层时间内存，这是一种皮质学习算法，最近已证明对高数据速率，多模式传感器和视频数据的分析和集成非常有效。它体现了各种组合搜索任务的许多特征，从而结合和扩展了贝叶斯网络，聚类和决策树的技术。 该项目是第一个评估“增强自动机”范式加速皮质学习算法的能力的项目，也是第一个探索自动机处理器功能的能力。在评估加速皮质学习算法的最佳方法的过程中，该项目将洞悉自动机处理器对其他人工智能算法的适用性。它还将导致开发新算法，软件库，编程指南和新的编程接口，以帮助加快对自动机处理器和未来加速器的其他应用程序的映射。它还将产生技术，以提高未来加速器的性能，灵活性和能源效率，以及对具有不同加速器硬件单元的异质系统设计和编程的新见解。该项目有可能为新的加速框架奠定基础，该框架可以有效地解决一系列棘手的问题，并提高绩效和能源效率的官方级，并指导未来加速器的开发。由于这些加速能力，便携式，低功率人工智能解决方案可能会变得无处不在。 该项目创建了涉及基于加速器的计算的研究和产品开发的工具。 该项目通过新的课程材料和作业，动手的研究和培训机会以及新的学术行业合作来为教育和宣传做出贡献。