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

美光最近推出了一种新的计算加速器架构-自动机处理器，它以重要的新功能扩展了非确定性有限自动机的计算范例。该体系结构特别适合于涉及模式匹配的任务。初步结果表明，加速比可能高达1000倍，特别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。该项目使用皮质学习算法(即，受观察和/或大脑工作原理启发的机器学习算法)作为案例研究，评估了这种新型体系结构在加速组合搜索方面的适用性。到目前为止，皮质学习算法主要只在软件中实现，这导致解决方案速度慢、体积大、成本高且耗电，从而限制了它们的适用性。特别是，这个项目最初专注于加速分层时间记忆，这是一种皮质学习算法，最近被证明对于分析和集成高数据速率、多模式传感器和视频数据非常有效。它体现了各种组合搜索任务的许多特征，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”范例加速皮质学习算法能力的项目，也是第一个探索自动机处理器能力的项目之一。在评估加速皮质学习算法的最佳方法的过程中，该项目将深入了解Automata处理器对其他人工智能算法的适用性。它还将导致开发新的算法、软件库、编程指南和新的编程接口，以帮助加快其他应用程序到Automata处理器和未来加速器的映射。它还将产生提高未来加速器的性能、灵活性和能效的技术，以及对具有不同加速器硬件单元的不同系统的设计和编程的新见解。该项目有可能为一种新的加速框架奠定基础，该框架能够有效地解决一大批棘手的问题，并在性能和能源效率方面进行数量级的改进，并指导未来加速器的开发。作为这些加速能力的结果，便携式、低功耗的人工智能解决方案可能会变得无处不在。该项目创建了一些工具，以促进涉及基于加速器的计算的研究和产品开发。该项目通过新的课程材料和作业、在尖端加速范例方面的实践研究和培训机会以及新的学术和行业合作，为教育和推广做出贡献。

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.