{"title":"WASP:高效电源管理,实现感知工作负载、自供电的人工智能物联网设备","authors":"Xiaofeng Hou;Xuehan Tang;Jiacheng Liu;Chao Li;Luhong Liang;Kwang-Ting Cheng","doi":"10.1109/TPDS.2024.3408167","DOIUrl":null,"url":null,"abstract":"The wide adoption of edge AI has heightened the demand for various battery-less and maintenance-free smart systems. Nevertheless, emerging Artificial Intelligence of Things (AIoT) are complex workloads showing increased power demand, diversified power usage patterns, and unique sensitivity to power management (PM) approaches. Existing AIoT devices cannot select the most appropriate PM tuning knob, and therefore they often make sub-optimal decisions. In addition, these PM solutions always assume traditional power regulation circuit which incurs non-negligible power loss and control overhead. This can greatly compromise the potential of AIoT efficiency. In this paper, we explore power management (PM) optimization for emerging self-powered AIoT devices. We propose WASP, a highly efficient power management scheme for workload-aware, self-powered AIoT devices. The novelty of WASP is two fold. First, it combines offline profiling and light-weight online control to select the most appropriate PM tuning knobs for the given DNN models. Second, it is well tailored to a reconfigurable voltage regulation module that can make the best use of the limited power budget. Our results show that WASP allows AIoT devices to accomplish 65.6% more inference tasks under a stringent power budget without any performance degradation compared with other existing approaches.","PeriodicalId":13257,"journal":{"name":"IEEE Transactions on Parallel and Distributed Systems","volume":"35 8","pages":"1400-1414"},"PeriodicalIF":5.6000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"WASP: Efficient Power Management Enabling Workload-Aware, Self-Powered AIoT Devices\",\"authors\":\"Xiaofeng Hou;Xuehan Tang;Jiacheng Liu;Chao Li;Luhong Liang;Kwang-Ting Cheng\",\"doi\":\"10.1109/TPDS.2024.3408167\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The wide adoption of edge AI has heightened the demand for various battery-less and maintenance-free smart systems. Nevertheless, emerging Artificial Intelligence of Things (AIoT) are complex workloads showing increased power demand, diversified power usage patterns, and unique sensitivity to power management (PM) approaches. Existing AIoT devices cannot select the most appropriate PM tuning knob, and therefore they often make sub-optimal decisions. In addition, these PM solutions always assume traditional power regulation circuit which incurs non-negligible power loss and control overhead. This can greatly compromise the potential of AIoT efficiency. In this paper, we explore power management (PM) optimization for emerging self-powered AIoT devices. We propose WASP, a highly efficient power management scheme for workload-aware, self-powered AIoT devices. The novelty of WASP is two fold. First, it combines offline profiling and light-weight online control to select the most appropriate PM tuning knobs for the given DNN models. Second, it is well tailored to a reconfigurable voltage regulation module that can make the best use of the limited power budget. Our results show that WASP allows AIoT devices to accomplish 65.6% more inference tasks under a stringent power budget without any performance degradation compared with other existing approaches.\",\"PeriodicalId\":13257,\"journal\":{\"name\":\"IEEE Transactions on Parallel and Distributed Systems\",\"volume\":\"35 8\",\"pages\":\"1400-1414\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Parallel and Distributed Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10546261/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Parallel and Distributed Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10546261/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
WASP: Efficient Power Management Enabling Workload-Aware, Self-Powered AIoT Devices
The wide adoption of edge AI has heightened the demand for various battery-less and maintenance-free smart systems. Nevertheless, emerging Artificial Intelligence of Things (AIoT) are complex workloads showing increased power demand, diversified power usage patterns, and unique sensitivity to power management (PM) approaches. Existing AIoT devices cannot select the most appropriate PM tuning knob, and therefore they often make sub-optimal decisions. In addition, these PM solutions always assume traditional power regulation circuit which incurs non-negligible power loss and control overhead. This can greatly compromise the potential of AIoT efficiency. In this paper, we explore power management (PM) optimization for emerging self-powered AIoT devices. We propose WASP, a highly efficient power management scheme for workload-aware, self-powered AIoT devices. The novelty of WASP is two fold. First, it combines offline profiling and light-weight online control to select the most appropriate PM tuning knobs for the given DNN models. Second, it is well tailored to a reconfigurable voltage regulation module that can make the best use of the limited power budget. Our results show that WASP allows AIoT devices to accomplish 65.6% more inference tasks under a stringent power budget without any performance degradation compared with other existing approaches.
期刊介绍:
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.