The University of Southampton
University of Southampton Institutional Repository

Learning-based BTI stress estimation and mitigation in multi-core processor systems

Learning-based BTI stress estimation and mitigation in multi-core processor systems
Learning-based BTI stress estimation and mitigation in multi-core processor systems
With the increasing demand of designing a reliable processing devices, the issue of CMOS ageing is jeopardising the industry of digital devices. Many studies has been cover this area for modelling the ageing behaviour at the device level or developing ageing sensors for on-line delay detection at the system level. However, we are presenting a method to estimate the ageing stresses (e.g. Temperature, Ageing Stress Activity) rather than the modelling ageing (performance degradation) itself. The purpose for estimating the ageing stress is to optimise the system utilisation with the minimisation of ageing stress. In multicore processors, the existence of more than one source of ageing stress is higher than single core processor but the optimisation space is higher as well along with the temperature and power optimisation. In this paper, we have modelled the ageing stress from the application level using machine learning techniques to train data extracted from high level workloads ( e.g. parsec and splash2 benchmarks) on four cores processor from Xeon. The ageing stress model is able to estimate the ageing stress with 0.1% error and is able to proactively reduce the ageing stress by 50%.
Abbas, Haider Muhi
74bc6951-c643-48dd-91b3-f32b570d4e60
Zwolinski, Mark
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Abbas, Haider Muhi
74bc6951-c643-48dd-91b3-f32b570d4e60
Zwolinski, Mark
adfcb8e7-877f-4bd7-9b55-7553b6cb3ea0
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33

Abbas, Haider Muhi, Zwolinski, Mark and Halak, Basel (2020) Learning-based BTI stress estimation and mitigation in multi-core processor systems. Microprocessors and Microsystems, 81. (doi:10.1016/j.micpro.2020.103713).

Record type: Article

Abstract

With the increasing demand of designing a reliable processing devices, the issue of CMOS ageing is jeopardising the industry of digital devices. Many studies has been cover this area for modelling the ageing behaviour at the device level or developing ageing sensors for on-line delay detection at the system level. However, we are presenting a method to estimate the ageing stresses (e.g. Temperature, Ageing Stress Activity) rather than the modelling ageing (performance degradation) itself. The purpose for estimating the ageing stress is to optimise the system utilisation with the minimisation of ageing stress. In multicore processors, the existence of more than one source of ageing stress is higher than single core processor but the optimisation space is higher as well along with the temperature and power optimisation. In this paper, we have modelled the ageing stress from the application level using machine learning techniques to train data extracted from high level workloads ( e.g. parsec and splash2 benchmarks) on four cores processor from Xeon. The ageing stress model is able to estimate the ageing stress with 0.1% error and is able to proactively reduce the ageing stress by 50%.

Text
Learning-based BTI stress estimation and mitigation in multi-core processor systems - Accepted Manuscript
Restricted to Repository staff only until 16 December 2022.
Request a copy

More information

Accepted/In Press date: 16 December 2020
e-pub ahead of print date: 19 December 2020

Identifiers

Local EPrints ID: 446427
URI: http://eprints.soton.ac.uk/id/eprint/446427
PURE UUID: e8842472-7e7a-471f-acb9-ba4840799fe1
ORCID for Mark Zwolinski: ORCID iD orcid.org/0000-0002-2230-625X
ORCID for Basel Halak: ORCID iD orcid.org/0000-0003-3470-7226

Catalogue record

Date deposited: 09 Feb 2021 17:31
Last modified: 15 Sep 2021 01:58

Export record

Altmetrics

Contributors

Author: Haider Muhi Abbas
Author: Mark Zwolinski ORCID iD
Author: Basel Halak ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×