Collaborative Research: CNS Core: Medium: Exploiting Synergies Between Machine-Learning Algorithms and Hardware Heterogeneity for High-Performance and Reliable Manycore Computing

先进的计算系统长期以来一直是科学、工程和新技术突破的推动者。然而，随着摩尔定律的放缓，以及深度学习、图形分析和科学模拟等大数据应用的不断需求，目前的解决方案并不足够。需要创新的计算机体系结构和高效的计算方法来设计特定于应用程序的硬件系统，以优化性能、功耗和可靠性。这项工作的主要重点是设计和演示一个异构的单芯片多核平台，通过片上网络集成CPU， GPU，加速器和内存核心，以避免昂贵的片外数据传输。该项目的目标是解决特定应用的异构多核系统的设计问题，为大数据应用实现前所未有的性能和能效水平。pi将通过出版物、研讨会、教程和讲习班传播研究成果。该项目还将开发跨学科的研究型课程，整合计算机体系结构、机器学习和数据驱动的设计优化。参与这项研究的本科生和研究生将接受培训，将课堂知识应用于需要下一代硬件、软件和理论专业知识的研究问题。该项目将为大数据应用的新型计算范式奠定基础，使我们能够快速设计和自主管理异构多核计算系统，以提高性能、降低功耗并增强可靠性。内存中处理可以克服内存墙，但它给特定于应用程序的整体系统优化带来了新的挑战。具体研究任务包括：1)数据驱动的异构多核架构多目标设计空间探索与优化算法；2)可靠性评估与可靠性系统设计；3)面向自主资源管理的结构化学习框架；4)基于新兴大数据应用负载的性能、功耗和可靠性评估。该框架将结合多目标设计空间探索和优化、计算和通信的异构性以及数据驱动算法的优势，以提高多核心平台的性能、能效和可靠性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Advanced computing systems have long been enablers for breakthroughs in science, engineering, and new technologies. However, with the slowing down of Moore’s law and the relentless needs of Big-Data applications, e.g., deep learning, graph analytics, and scientific simulations, current solutions are not adequate. There is a need for innovative computer architectures and computationally efficient methods to design application-specific hardware systems to optimize performance, power consumption, and reliability. The main focus of this work is design and demonstration of a heterogeneous single-chip manycore platform, integrating CPU, GPU, accelerator, and memory cores, via a network-on-chip to avoid expensive off-chip data transfers. The goal of this project is to address the design of application-specific heterogeneous manycore systems that are poised to achieve unprecedented levels of performance and energy-efficiency for Big-Data applications. The PIs will disseminate research outcomes via publications, seminars, tutorials, and workshops. The project is also leading to the development of an interdisciplinary research-based curriculum integrating computer architectures, machine learning, and data-driven design optimization. Undergraduate and graduate students involved in this research will be trained to apply classroom knowledge to research problems that require next-generation hardware, software, and theoretical expertise. The project will lay the foundations for a novel computing paradigm for Big-Data applications that allows us to quickly design and autonomously self-manage heterogeneous manycore computing systems to improve performance, reduce power consumption, and enhance reliability. In-memory processing can overcome the memory wall, but it introduces new challenges in overall application-specific system optimization. The specific research tasks include: 1) Data-driven multi-objective design space exploration and optimization algorithms for heterogeneous manycore architectures; 2) Reliability assessment and system design for reliability; 3) Structured learning framework for autonomous resource management; and 4) Performance, power, and reliability evaluation using emerging Big-Data application workloads. This framework will combine the benefits of multi-objective design space exploration and optimization, heterogeneity in computation and communication, and data-driven algorithms to improve performance, energy-efficiency, and reliability of manycore platforms.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.