嵌入式软件算法级功耗BP网络模型研究  

Research on Embedded Software Power Model of Algorithm Level Using BP Neural Networks

作　　者：刘啸滨[1] 郭兵[1] 沈艳[2] 王继禾[1] 伍元胜[1] 

机构地区：[1]四川大学计算机学院,成都610065 [2]成都信息工程学院控制工程学院,成都610225

出　　处：《电子科技大学学报》2011年第6期921-926,共6页Journal of University of Electronic Science and Technology of China

基　　金：国家863计划(2008AA01Z105);四川省杰出青年科技基金(2010JQ0011)

摘　　要：从算法级分析软件功耗和软件特征的关联关系,对嵌入式软件的时间复杂度、空间复杂度和输入规模3个特征进行度量,提出一种基于算法复杂度的嵌入式软件功耗宏模型。设计、训练一种BP神经网络,用于实现功耗函数逼近。仿真实验表明,该功耗函数的估算结果和真实值误差在10%以内,可用于快速估算软件算法在一定输入规模情况下的功耗值,为下一步开展功耗优化工作打下基础。Power consumption in embedded systems is becoming a hot issue that attracts more attention. Different assemble instruction set, software algorithm, and high-level software architecture can significantly affect the system energy consumption. In this paper, we firstly analyze the relations between software energy consumption and some software characteristics on algorithm level. Through measuring three software characteristics, i.e., average time complexity, space complexity, and input scale, we propose a BP neural network software power model based on algorithm complexity. Then, we design and train a kind of BP neural network to accomplish energy consumption function approximation. Simulation experiment results show that the error between the estimation value of this energy consumption function and the real energy consumption value is below 10%. Therefore, it could quickly estimate the energy consumption of software in some input scale, which is an important fundament to explore the energy consumption optimization in the future.

关 键 词：算法级 BP神经网络 复杂度 嵌入式软件 功耗模型 

分 类 号：TP302.7[自动化与计算机技术—计算机系统结构]