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Embed. Comput. Syst."],"published-print":{"date-parts":[[2020,5,31]]},"abstract":"<jats:p>\n            Video object detection and action recognition typically require deep neural networks (DNNs) with huge number of parameters. It is thereby challenging to develop a DNN video comprehension unit in resource-constrained terminal devices. In this article, we introduce a deeply tensor-compressed video comprehension neural network, called DEEPEYE, for inference on terminal devices. Instead of building a Long Short-Term Memory (LSTM) network directly from high-dimensional raw video data input, we construct an LSTM-based spatio-temporal model from structured, tensorized time-series features for object detection and action recognition. A deep compression is achieved by tensor decomposition and trained quantization of the time-series feature-based LSTM network. We have implemented DEEPEYE on an ARM-core-based IOT board with 31 FPS consuming only 2.4W power. Using the video datasets MOMENTS, UCF11 and HMDB51 as benchmarks, DEEPEYE achieves a 228.1\u00d7 model compression with only 0.47% mAP reduction; as well as 15\n            <jats:italic>k<\/jats:italic>\n            \u00d7 parameter reduction with up to 8.01% accuracy improvement over other competing approaches.\n          <\/jats:p>","DOI":"10.1145\/3381805","type":"journal-article","created":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T18:00:15Z","timestamp":1590429615000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["DEEPEYE"],"prefix":"10.1145","volume":"19","author":[{"given":"Yuan","family":"Cheng","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Guangya","family":"Li","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}]},{"given":"Ngai","family":"Wong","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7046-3455","authenticated-orcid":false,"given":"Hai-Bao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Hao","family":"Yu","sequence":"additional","affiliation":[{"name":"Southern University of Science and Technology, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2020,5,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"International Conference on Machine Learning. 2285--229","author":"Chen Wenlin","year":"2015","unstructured":"Wenlin Chen , James Wilson , Stephen Tyree , Kilian Weinberger , and Yixin Chen . 2015 . 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