面向边缘智能应用的多出口深度神经网络随机优化方法  

Stochastic Optimization Method for Multi-exit Deep Neural Networks for Edge Intelligence Applications

作　　者：李洲诚 张毅 孙晋[1] LI Zhoucheng;ZHANG Yi;SUN Jin(School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学计算机科学与工程学院,南京210094

出　　处：《计算机科学》2025年第4期85-93,共9页Computer Science

基　　金：江苏省重点研发计划(批准号:BE2022065-2);江苏省创新支撑计划(批准号:BZ2023046)。

摘　　要：边缘智能作为一种新型的智能计算范式,能够有效提升智能推理任务在嵌入式边缘设备中的响应速度。而信息年龄(AoI)作为衡量数据时效性的重要指标,对于边缘智能应用的计算资源开销和实时响应至关重要。针对多出口深度神经网络(DNN)的资源配置优化问题,考虑出口退出概率造成的AoI随机不确定性,引入系统AoI的概率约束,基于随机优化理论对出口设置进行决策,以最小化多出口DNN的资源开销。文中提出了一种基于布谷鸟搜索的元启发式算法对所构建的具有概率约束的随机优化问题进行求解,基于各出口的退出概率预测系统AoI的统计分布,根据给定的AoI阈值计算相应的资源消耗量并将其作为布谷鸟个体的适应度值,迭代更新布谷鸟种群并搜索得到最小计算资源开销的出口设置方案。针对多种DNN模型的实验结果表明,与确定性的优化方法相比,随机优化方法能够获得更佳的出口设置决策,在满足AoI概率约束的前提下显著降低了DNN的计算开销。As a novel intelligent computing paradigm,edge intelligence can effectively enhance the response speed of intelligent inference tasks on embedded edge devices.Age of information(AoI),an important metric for measuring data freshness,is of great significance to the computing resource overhead and real-time response of edge intelligence applications.This work studies the resource allocation optimization problem for multi-exit deep neural networks(DNNs)that takes into account the uncertainty of AoI caused by exit probabilities and introduces a probabilistic constraint on system AoI.The stochastic optimization theory is incorporated to make decision on the most appropriate exit configuration for the purpose of minimizing the resource overhead of multi-exit DNNs.A cuckoo search-based metaheuristic algorithm is proposed to solve the stochastic optimization problem with the probabilistic AoI constraint.The metaheuristic predicts the statistical distribution of system AoI based on the exit probabilities,calculates the resource consumption according to a specified AoI threshold and uses it as the fitness value of the corresponding cuckoo individual,and iteratively updates the cuckoo population to explore the exit configuration solution leading to the lowest computing resource overhead.Experimental results on various DNN models show that compared with deterministic optimization methods,the stochastic optimization approach can produce better exit configuration solutions,significantly reducing resource overhead while satisfying the probabilistic AoI constraint.

关 键 词：边缘智能 信息年龄 多出口神经网络 随机优化 概率约束 元启发式算法 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]