Dynamic tuning of configurable architectures: the AWW online algorithm

Authors:
Chen Huang;David Sheldon;Frank Vahid
Affiliations:
University of California, Riverside, Riverside, CA, USA;University of California, Riverside, Riverside, CA, USA;University of California, Riverside, Riverside, CA, USA
Venue:
CODES+ISSS '08 Proceedings of the 6th IEEE/ACM/IFIP international conference on Hardware/Software codesign and system synthesis
Year:
2008

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Abstract

Architectures with software-writable parameters, or configurable architectures, enable runtime reconfiguration of computing platforms to the applications they execute. Such dynamic tuning can improve application performance, as well as energy. However, reconfiguring incurs a temporary performance cost. Thus, online algorithms are needed that decide when to reconfigure and which configuration to choose such that overall performance is optimized. We introduce the adaptive weighted window (AWW) algorithm, and compare with several other algorithms, including algorithms previously developed by the online algorithm community. We describe experiments showing that AWW results are within 4% of the offline optimal on average. AWW outperforms the other algorithms, and is robust across three datasets and across three categories of application sequences too. AWW improves a non-dynamic approach on average by 6%, and by up to 30% in low-reconfiguration-time situations.