Instruction level power analysis and optimization of software
Journal of VLSI Signal Processing Systems - Special issue on technologies for wireless computing
Combinatorial optimization
Compiler optimization on instruction scheduling for low power
ISSS '00 Proceedings of the 13th international symposium on System synthesis
Energy efficient CMOS microprocessor design
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Incorporating speculative execution into scheduling of control-flow-intensive designs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
DSPxPlore: design space exploration methodology for an embedded DSP core
Proceedings of the 2004 ACM symposium on Applied computing
Algorithms and analysis of scheduling for loops with minimum switching
International Journal of Computational Science and Engineering
Algorithms and analysis of scheduling for low-power high-performance DSP on VLIW processors
International Journal of High Performance Computing and Networking
ACM SIGARCH Computer Architecture News - Special issue: ALPS '07---advanced low power systems
Energy-Aware Loop Scheduling and Assignment for Multi-Core, Multi-Functional-Unit Architecture
Journal of Signal Processing Systems
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In this paper, for low-power embedded systems, we solve the instruction scheduling and reordering problem as a Precedence Constrained Hamiltonian Path Problem for DAGs and the Traveling Salesman Problem (TSP), both of which are NP-Hard [1,2]. We propose an efficient instruction-level optimization algorithm for solving the NP-Hard problem. Minimum spanning tree (MST) and simulated annealing (SA) mechanisms are used for the optimization. We describe the methods for generating the control flow and data dependence graph (CDG), power dissipation table (PDT), and weighted strongly connected graph (SCG) for the instruction-level low-power analysis. In addition, confidence limits with error tolerance are considered for the validation of the optimization. Finally, experimental results that demonstrate the effectiveness and the efficiency of the proposed algorithms are shown.