Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
作者： Zhi Zhou , Xu Chen , En Li , Liekang Zeng , Ke Luo
DOI: 10.1109/JPROC.2019.2918951
关键词: Recommender system 、 Mobile computing 、 Computer science 、 Enhanced Data Rates for GSM Evolution 、 Edge device 、 Big data 、 Edge computing 、 Artificial intelligence 、 Deep learning 、 The Internet
摘要: With the breakthroughs in deep learning, recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems video/audio surveillance. More recently, with proliferation mobile computing Internet Things (IoT), billions IoT devices are connected Internet, generating zillions bytes data at network edge. Driving by this trend, there is an urgent need push AI frontiers edge so as fully unleash potential big data. To meet demand, computing, emerging paradigm that pushes tasks services core edge, has been widely recognized promising solution. The resulted new interdiscipline, or (EI), beginning receive tremendous amount interest. However, research on EI still its infancy stage, dedicated venue for exchanging advances highly desired both computer system communities. end, we conduct comprehensive survey efforts EI. Specifically, first review background motivation running We then provide overview overarching architectures, frameworks, key technologies learning model toward training/inference Finally, discuss future opportunities believe will elicit escalating attentions, stimulate fruitful discussions, inspire further ideas
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