DocumentCode :
1820075
Title :
Smart cache: A self adaptive cache architecture for energy efficiency
Author :
Sundararajan, Karthik T. ; Jones, Timothy M. ; Topham, Nigel
Author_Institution :
Sch. of Inf., Univ. of Edinburgh, Edinburgh, UK
fYear :
2011
fDate :
18-21 July 2011
Firstpage :
41
Lastpage :
50
Abstract :
The demand for low-power embedded systems requires designers to tune processor parameters to avoid excessive energy wastage. Tuning on a per-application or per-application-phase basis allows a greater saving in energy consumption without a noticeable degradation in performance. On-chip caches often consume a significant fraction of the total energy budget and are therefore prime candidates for adaptation. Fixed-configuration caches must be designed to deliver low average memory access times across a wide range of potential applications. However, this can lead to excessive energy consumption for applications that do not require the full capacity or associativity of the cache at all times. Furthermore, in systems where the clock period is constrained by the access times of level-1 caches, the clock frequency for all applications is effectively limited by the cache requirements of the most demanding phase within the most demanding application. This results in both performance and energy efficiency that represents the lowest common denominator across the applications. In this paper we present a Set and way Management cache Architecture for Run-Time reconfiguration (Smart cache), a cache architecture that allows reconfiguration in both its size and associativity. Results show the energy-delay of the Smart cache is on average 14% better than state-of-the-art cache reconfiguration architectures. We then leverage the flexibility provided by our cache to dynamically reconfigure the hierarchy as a program runs. We develop a decision tree based machine learning model to control the adaptation and automatically reconfigure the cache to the best configuration. Results show an average reduction in energy-delay product of 17% in the data cache (just 1% away from an oracle result) and 34% in the level-2 cache (just 5% away from an oracle), with an overall performance degradation of less than 2% compared with a baseline statically-configured cache.
Keywords :
cache storage; decision trees; embedded systems; energy conservation; learning (artificial intelligence); memory architecture; reconfigurable architectures; cache requirement; clock frequency; decision tree based machine learning model; energy efficiency; energy wastage; fixed-configuration cache; level-1 cache; low average memory access customization; low-power embedded system; management cache architecture; on-chip cache; run-time reconfiguration; selfadaptive cache architecture; smart cache; state-of-the-art cache reconfiguration; tune processor parameter; Benchmark testing; Computer architecture; Decision trees; Educational institutions; Energy consumption; Monitoring; Training;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Embedded Computer Systems (SAMOS), 2011 International Conference on
Conference_Location :
Samos
Print_ISBN :
978-1-4577-0802-2
Electronic_ISBN :
978-1-4577-0801-5
Type :
conf
DOI :
10.1109/SAMOS.2011.6045443
Filename :
6045443
Link To Document :
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