摘要
As semi-conductor technologies move down to the nanometer scale, leakage power has become a significant component of the total power consumption. In this paper, we present a leakage-aware modulo scheduling algorithm to achieve leakage energy saving for applications with loops on Very Long Instruction Word (VLIW) architectures. The proposed algorithm is designed to maximize the idleness of function units integrated with the dual-threshold domino logic, and reduce the number of transitions between the active and sleep modes. We have implemented our technique in the Trimaran compiler and conducted experiments using a set of embedded benchmarks from DSPstone and Mibench on the cycle-accurate VLIW simulator of Trimaran. The results show that our technique achieves significant leakage energy saving compared with a previously published DAG-based (Directed Acyclic Graph) leakage-aware scheduling algorithm.
As semi-conductor technologies move down to the nanometer scale, leakage power has become a significant component of the total power consumption. In this paper, we present a leakage-aware modulo scheduling algorithm to achieve leakage energy saving for applications with loops on Very Long Instruction Word (VLIW) architectures. The proposed algorithm is designed to maximize the idleness of function units integrated with the dual-threshold domino logic, and reduce the number of transitions between the active and sleep modes. We have implemented our technique in the Trimaran compiler and conducted experiments using a set of embedded benchmarks from DSPstone and Mibench on the cycle-accurate VLIW simulator of Trimaran. The results show that our technique achieves significant leakage energy saving compared with a previously published DAG-based (Directed Acyclic Graph) leakage-aware scheduling algorithm.
基金
supported by the National Natural Science Foundation of China under Grant Nos. 60873006 and 61070049
the International Collaborative Research Program under Grant No. 2010DFB10930
the Beijing Science Foundation under Grant No.KZ200910028007
the Australian Research Council (ARC) under Grant No. DP0881330