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However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then \"the neighbor effect\" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.<\/jats:p>","DOI":"10.1145\/3478073","type":"journal-article","created":{"date-parts":[[2021,9,14]],"date-time":"2021-09-14T22:48:23Z","timestamp":1631659703000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Context-aware Adaptive Surgery"],"prefix":"10.1145","volume":"5","author":[{"given":"Hongli","family":"Wang","sequence":"first","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, Shaanxi, China"}]},{"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, Shaanxi, China"}]},{"given":"Jiaqi","family":"Liu","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, Shaanxi, China"}]},{"given":"Sicong","family":"Liu","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, Shaanxi, China"}]},{"given":"Yungang","family":"Wu","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, Shaanxi, China"}]},{"given":"Zhiwen","family":"Yu","sequence":"additional","affiliation":[{"name":"Northwestern Polytechnical University, Xi'an, Shaanxi, China"}]}],"member":"320","published-online":{"date-parts":[[2021,9,14]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"160","article-title":"An effective deep autoencoder approach for online smartphone-based human activity recognition","volume":"17","author":"Almaslukh Bandar","year":"2017","unstructured":"Bandar Almaslukh , Jalal AlMuhtadi , and Abdelmonim Artoli . 2017 . 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