Statistical Machine Learning based Modeling Framework for Design Space Exploration and Run-time Cross-stack Energy Optimization for Many-core Processors

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Zhang, C. (2013). Statistical Machine Learning based Modeling Framework for Design Space Exploration and Run-time Cross-stack Energy Optimization for Many-core Processors. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

The complexity of many-core processors continues to grow as a larger number of heterogeneous cores are integrated on a single chip. Such systems-on-chip contains computing structures ranging from complex out-of-order cores, simple in-order cores, digital signal processors (DSPs), graphic processing units (GPUs), application specific processors, hardware accelerators, I/O subsystems, network-on-chip interconnects, and large caches arranged in complex hierarchies. While the industry focus is on putting higher number of cores on a single chip, the key challenge is to optimally architect these many-core processors such that performance, energy and area constraints are satisfied. The traditional approach to processor design through extensive cycle accurate simulations are ill-suited for designing many-core processors due to the large microarchitecture design space that must be explored. Additionally it is hard to optimize such complex processors and the applications that run on them statically at design time such that performance and energy constraints are met under dynamically changing operating conditions.The dissertation establishes statistical machine learning based modeling framework that enables the efficient design and operation of many-core processors that meets performance, energy and area constraints. We apply the proposed framework to rapidly design the microarchitecture of a many-core processor for multimedia, computer graphics rendering, finance, and data mining applications derived from the Parsec benchmark. We further demonstrate the application of the framework in the joint run-time adaptation of both the application and microarchitecture such that energy availability constraints are met.

Details
Author

Zhang, Changshu

Title

Statistical Machine Learning based Modeling Framework for Design Space Exploration and Run-time Cross-stack Energy Optimization for Many-core Processors

Physical Description

1 online resource (132 pages) : PDF

Date

2013

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

The complexity of many-core processors continues to grow as a larger number of heterogeneous cores are integrated on a single chip. Such systems-on-chip contains computing structures ranging from complex out-of-order cores, simple in-order cores, digital signal processors (DSPs), graphic processing units (GPUs), application specific processors, hardware accelerators, I/O subsystems, network-on-chip interconnects, and large caches arranged in complex hierarchies. While the industry focus is on putting higher number of cores on a single chip, the key challenge is to optimally architect these many-core processors such that performance, energy and area constraints are satisfied. The traditional approach to processor design through extensive cycle accurate simulations are ill-suited for designing many-core processors due to the large microarchitecture design space that must be explored. Additionally it is hard to optimize such complex processors and the applications that run on them statically at design time such that performance and energy constraints are met under dynamically changing operating conditions.The dissertation establishes statistical machine learning based modeling framework that enables the efficient design and operation of many-core processors that meets performance, energy and area constraints. We apply the proposed framework to rapidly design the microarchitecture of a many-core processor for multimedia, computer graphics rendering, finance, and data mining applications derived from the Parsec benchmark. We further demonstrate the application of the framework in the joint run-time adaptation of both the application and microarchitecture such that energy availability constraints are met.

Genre

doctoral dissertations

Subjects--Topics

Electrical engineering
Computer engineering

Degree

Ph.D.

Keywords

Cross-Stack
Energy
Many-Core
Modeling
Optimization
Run-Time

Subject Area

Electrical Engineering

Advisor(s)

Ravindran, Arun

Committee Members

Mukherjee, Arindam
Joshi, Bharat
Diao, Yuanan

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2013.

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Rights Holder Information

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Identifier

Zhang_uncc_0694D_10436

Permalink

http://hdl.handle.net/20.500.13093/etd:1477