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和大数据应用程序的不断需求，例如，深度学习、图形分析和科学模拟，目前的解决方案是不够的。需要创新的计算机架构和计算高效的方法来设计专用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众核平台，集成CPU，GPU，加速器和内存内核，通过片上网络，以避免昂贵的片外数据传输。该项目的目标是解决特定于应用程序的异构众核系统的设计，这些系统有望为大数据应用程序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。该项目还导致了一个跨学科的研究为基础的课程，整合计算机体系结构，机器学习和数据驱动的设计优化的发展。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程序的新型计算模式奠定基础，使我们能够快速设计和自主管理异构众核计算系统，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，但它在整体特定于应用程序的系统优化中引入了新的挑战。具体研究任务包括：1）异构众核架构的数据驱动多目标设计空间探索和优化算法; 2）可靠性评估和可靠性系统设计; 3）自主资源管理的结构化学习框架; 4）使用新兴的大数据应用工作负载进行性能，功耗和可靠性评估。该框架将结合联合收割机的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好处，以提高性能，能源效率和众核平台的可靠性。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.