Reconfigurable Accelerators for Emerging Machine Learning Workloads

高性能节能计算的可用性在许多科学和工程领域都是必不可少的。机器学习（ML）领域尤其如此。在过去的几年里，这种计算一直是该领域带来的许多创新的推进器，特别是在视觉数据的处理方面（例如，面部识别）。高性能节能计算通常由基于加速器的系统提供，该系统将通用处理器与自定义加速器相结合，例如图形处理单元（GPU）、现场可编程门阵列（FPGA）或神经处理单元（NPU）。本研究的重点是设计和有效使用下一代机器学习加速器。如今，ML加速器专注于提高密集矩阵计算的性能，这在卷积神经网络（CNN）ML模型中随处可见。然而，有新兴的ML模型，利用不是密集矩阵计算形式的计算。示例包括图卷积神经网络（GCNN），其从表示实体及其之间的关系的数据（例如，社交媒体上的用户及其交互或研究出版物及其引用），嵌入使用自然语言处理模型，以及自动驾驶车辆中使用的对象识别模型。这些新兴的ML工作负载需要新的加速器设计。因此，本研究的目标是保持“领先”，并探索新的加速器设计及其对这些新兴ML工作负载的软件支持。这项研究有若干相互关联的主题。一个主题是探索上述新兴工作负载的可重构加速器的架构。重新配置允许加速器根据手头的ML工作负载量身定制，这更有效。第二个主题是探索在加速器上运行这些工作负载所需的工具，特别是将工作负载映射到加速器单元的工具。第三个主题是设计新的编译器优化，以提高目标加速器上的代码质量。成功完成拟议的研究将有助于ML加速器设计和编译器优化领域的知识。此外，该工作开发的加速器和编译器支持可供探索新兴ML模型的研究人员使用，从而能够快速探索GCNN，从而间接促进该领域的进步。该研究将对研究生和本科生进行计算机科学三个领域的交叉培训：加速器架构、机器学习和系统软件。具有这种综合专业知识的人员今天需求量很大。

The availability of high-performance energy-efficient computing is essential in many fields of science and engineering. This is particularly the case for the field of Machine Learning (ML). Such computing has been a propeller for the many innovations this field has brought over the past few years, particularly in the processing of visual data (e.g., facial recognition). High-performance energy-efficient computing is often delivered by accelerator-based systems that combine a general-purpose processor with a custom accelerator, such as a Graphics Processing Unit (GPU), a Field Programmable Gate Array (FPGA), or a Neural Processing Unit (NPU). This research focuses on the design and effective use of next-generation accelerators for machine learning. Today, ML accelerators focus on improving the performance of dense matrix computations, ubiquitously found in Convolutional Neural Network (CNN) ML models. However, there are emerging ML models that utilize computations that are not in the form of dense matrix calculations. Examples include Graph Convolutional Neural Networks (GCNNs) that learn from data which represents entities and relationships among them (e.g., users on social media and their interactions or research publications and their citations), embeddings used natural language processing models, and object identification models used in autonomous vehicles. These emerging ML workloads demand new accelerator designs. Thus, the goal of this research is to stay "ahead-of-the curve" and explore a novel accelerator design and its software support for these emerging ML workloads. The research has a number of interrelated themes. One theme is to explore the architecture of a reconfigurable accelerator for the above emerging workloads. The reconfiguration allows an accelerator to be tailored to the ML workload at hand and this, more efficient. A second theme is to explore the tools that are needed to run these workloads on the accelerator, in particular ones for mapping of the workloads to the units of the accelerator. A third theme is the design new compiler optimizations that improve the quality of code on the target accelerator. Successful completion of the proposed research will contribute to knowledge in the areas of ML accelerator design and compiler optimizations. Further, the accelerators and compiler support developed by the work can be used by researchers who explore emerging ML models, enabling fast exploration of GCNNs and thus indirectly contributing to advances in this field. The research will train both graduate and undergraduate students in the intersection of three areas of computer science: accelerator architecture, machine learning and system software. Personnel with such combined expertise are in high demand today.