SEPAS: a highly accurate energy-efficient branch predictor
Proceedings of the 2004 international symposium on Low power electronics and design
Energy-efficient and high-performance instruction fetch using a block-aware ISA
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
Block-aware instruction set architecture
ACM Transactions on Architecture and Code Optimization (TACO)
Power efficient branch prediction through early identification of branch addresses
CASES '06 Proceedings of the 2006 international conference on Compilers, architecture and synthesis for embedded systems
Computational and storage power optimizations for the O-GEHL branch predictor
Proceedings of the 4th international conference on Computing frontiers
Thrifty BTB: A comprehensive solution for dynamic power reduction in branch target buffers
Microprocessors & Microsystems
ACM Transactions on Architecture and Code Optimization (TACO)
A power-aware alternative for the perceptron branch predictor
ACSAC'07 Proceedings of the 12th Asia-Pacific conference on Advances in Computer Systems Architecture
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We introduce Branch Predictor Prediction (BPP) as a power-aware branch prediction technique for high performance processors. Our predictor reduces branch prediction power dissipation by selectively turning on and off two of the three tables used in the combined branch predictor. BPP relies on a small buffer that stores the addresses and the sub-predictors used by the most recent branches executed. Later we refer to this buffer to decide if any of the sub-predictors and the selector could be gated without harming performance. In this work we study power and performance trade-offs for a subset of SPEC 2k benchmarks. We showthat on the average and for an 8-way processor, BPP can reduce branch prediction power dissipation by 28% and 14% compared to non-banked and banked 32k predictors respectively. This comes with a negligible impact on performance (1% max). We show that BPP always reduces power even for smaller predictors and that it offers better overall power and performance compared to simpler predictors.