The YAGS branch prediction scheme
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
The impact of delay on the design of branch predictors
Proceedings of the 33rd annual ACM/IEEE international symposium on Microarchitecture
Neural methods for dynamic branch prediction
ACM Transactions on Computer Systems (TOCS)
Automatically characterizing large scale program behavior
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Branch Predictor Prediction: A Power-Aware Branch Predictor for High-Performance Processors
ICCD '02 Proceedings of the 2002 IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD'02)
Power Issues Related to Branch Prediction
HPCA '02 Proceedings of the 8th International Symposium on High-Performance Computer Architecture
Improving Branch Prediction Accuracy by Reducing Pattern History Table Interference
PACT '96 Proceedings of the 1996 Conference on Parallel Architectures and Compilation Techniques
SEPAS: a highly accurate energy-efficient branch predictor
Proceedings of the 2004 international symposium on Low power electronics and design
Analysis of the O-GEometric History Length Branch Predictor
Proceedings of the 32nd annual international symposium on Computer Architecture
A power-aware alternative for the perceptron branch predictor
ACSAC'07 Proceedings of the 12th Asia-Pacific conference on Advances in Computer Systems Architecture
| Hi-index | 0.00 |
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 performance by up to 12% as we make faster prediction possible.