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Cool-Mem: combining statically speculative memory accessing with selective address translation for energy efficiency
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Source Architectural Support for Programming Languages and Operating Systems archive
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems table of contents
San Jose, California
SESSION: Energy efficient systems table of contents
Pages: 133 - 143  
Year of Publication: 2002
ISBN:1-58113-574-2
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Authors
Raksit Ashok  University of Massachusetts, Amherst, MA
Saurabh Chheda  University of Massachusetts, Amherst, MA
Csaba Andras Moritz  University of Massachusetts, Amherst, MA
Sponsors
SIGPLAN: ACM Special Interest Group on Programming Languages
SIGOPS: ACM Special Interest Group on Operating Systems
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper presents Cool-Mem, a family of memory system architectures that integrate conventional memory system mechanisms, energy-aware address translation, and compiler-enabled cache disambiguation techniques, to reduce energy consumption in general purpose architectures. It combines statically speculative cache access modes, a dynamic CAM based Tag-Cache used as backup for statically mispredicted accesses, various conventional multi-level associative cache organizations, embedded protection checking along all cache access mechanisms, as well as architectural organizations to reduce the power consumed by address translation in virtual memory. Because it is based on speculative static information, the approach removes the burden of provable correctness in compiler analysis passes that extract static information. This makes Cool-Mem applicable for large and complex applications, without having any limitations due to complexity issues in the compiler passes or the presence of precompiled static libraries. Based on extensive evaluation, for both SPEC2000 and Mediabench applications, 12% to 20% total energy savings are obtained in the processor, with performance ranging from 1.2% degradation to 8% improvement, for the applications studied.


REFERENCES

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Collaborative Colleagues:
Raksit Ashok: colleagues
Saurabh Chheda: colleagues
Csaba Andras Moritz: colleagues