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2007
ACM

Computational and storage power optimizations for the O-GEHL branch predictor

13 years 4 months ago
Computational and storage power optimizations for the O-GEHL branch predictor
In recent years, highly accurate branch predictors have been proposed primarily for high performance processors. Unfortunately such predictors are extremely energy consuming and in some cases not practical as they come with excessive prediction latency. One example of such predictors is the O-GEHL predictor. To achieve high accuracy, O-GEHL relies on large tables and extensive computations and requires high energy and long prediction delay. In this work we propose power optimization techniques that aim at reducing both computational complexity and storage size for the O-GEHL predictor. We show that by eliminating unnecessary data from computations, we can reduce both predictor’s energy consumption and delay. Moreover, we apply information theory findings to remove redundant storage, without any significant accuracy penalty. We reduce the dynamic and static power dissipated in the computational parts of the predictor by up to 74% and 65% respectively. Meantime we improve performanc...
Kaveh Aasaraai, Amirali Baniasadi, Ehsan Atoofian
Added 18 Oct 2010
Updated 18 Oct 2010
Type Conference
Year 2007
Where CF
Authors Kaveh Aasaraai, Amirali Baniasadi, Ehsan Atoofian
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