Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: SoftExplorer: Estimating and Optimizing the Power and Energy Consumption of a C Program for DSP Applications Eric Senn | EURASIP Journal on Applied Signal Processing 2005 16 2641-2654 2005 Hindawi Publishing Corporation SoftExplorer Estimating and Optimizing the Power and Energy Consumption of a C Program for DSP Applications Eric Senn LESTER University of South-Brittany BP 92116 56321 Lorient Cedex France Email Johann Laurent LESTER University of South-Brittany BP 92116 56321 Lorient Cedex France Email Nathalie Julien LESTER University of South-Brittany BP 92116 56321 Lorient Cedex France Email Eric Martin LESTER University of South-Brittany BP 92116 56321 Lorient Cedex France Email Received 30 January 2004 Revised 20 October 2004 We present a method to estimate the power and energy consumption of an algorithm directly from the C program. Three models are involved a model for the targeted processor the power model a model for the algorithm and a model for the compiler the prediction model . A functional-level power analysis is performed to obtain the power model. Five power models have been developed so far for different architectures from the simple RISC ARM7 to the very complex VLIW DSP TI C64. Important phenomena are taken into account like cache misses pipeline stalls and internal external memory accesses. The model for the algorithm expresses the algorithm s influence over the processor s activity. The prediction model represents the behavior of the compiler and how it will allow the algorithm to use the processor s resources. The data mapping is considered at that stage. We have developed a tool SoftExplorer which performs estimation both at the C-level and the assembly level. Estimations are performed on real-life digital signal processing applications with average errors of at the C-level and at the assembly level. We present how SoftExplorer can be used to optimize the consumption of an application. We first show how to find the best data mapping for an algorithm. .
