{"product_id":"deep-learning-on-edge-computing-devices-design-challenges-of-algorithm-and-architecture-9780323857833","title":"Deep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eDeep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture focuses on hardware architecture and embedded deep learning, presenting neural network algorithms and design optimization approaches for Edge-deep learning. It covers core concepts, theories, and algorithms and architecture optimization, with a comprehensive example of smart surveillance cameras. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 198 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 07 February 2022\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Elsevier - Health Sciences Division\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eDeep Learning on Edge Computing Devices: Design Challenges of Algorithm and Architecture delves into the realm of hardware architecture and embedded deep learning, encompassing neural networks. The title serves as a valuable resource for researchers seeking to optimize the performance of Edge-deep learning models for mobile computing and various other applications. By presenting neural network algorithms and hardware design optimization approaches, the book empowers researchers to maximize the potential of Edge-deep learning models.\u003cbr\u003e\u003cbr\u003eApplications are introduced in each section, providing a comprehensive overview of the field. A notable example, smart surveillance cameras, is presented at the end of the book, showcasing innovation in both algorithm and hardware architecture. The book is structured into three parts: core concepts, theories, and algorithms and architecture optimization.\u003cbr\u003e\u003cbr\u003eThis comprehensive text offers a solution for researchers seeking to enhance the efficiency of deep learning models on Edge-computing devices through collaborative design of algorithms and hardware. It serves as a valuable resource for scholars, researchers, and practitioners in the field of deep learning and edge computing.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 450g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 229 x 152 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9780323857833\u003c\/p\u003e","brand":"XichuanZhou,HaijunLiu,CongShi,JiLiu","offers":[{"title":"Paperback \/ softback","offer_id":44096362643706,"sku":"9780323857833","price":72.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_adbdc63c-7895-4b53-8304-0bb068fa49d7.jpg?v=1647362177","url":"https:\/\/shulphink.com\/products\/deep-learning-on-edge-computing-devices-design-challenges-of-algorithm-and-architecture-9780323857833","provider":"Shulph Ink","version":"1.0","type":"link"}