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

Washington State University, Pullman WA

Investigators

Abstract

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.