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Machine Learning for Design Space Exploration and Optimization of Manycore Systems

Published: 05 November 2018 Publication History

Abstract

In the emerging data-driven science paradigm, computing syStems ranging from IoT and mobile to manycores and datacenters play distinct roles. These systems need to be optimized for the objectives and constraints dictated by the needs of the application. In this paper, we describe how machine learning techniques can be leveraged to improve the computational-efficiency of hardware design optimization. This includes generic methodologies that are applicable for any hardware design space. As an example, we discuss a guided design space exploration framework to accelerate application-specific manycore systems design and advanced imitation learning techniques to improve on-chip resource management. We present some experimental results for application-specific manycore system design optimization and dynamic power management to demonstrate the efficacy of these methods over traditional EDA approaches.

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        2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
        Nov 2018
        939 pages

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        Published: 05 November 2018

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