Eugene: Towards Deep Intelligence as a Service
| Autor: | Archan Misra, Shengzhong Liu, Dongxin Liu, Yiran Zhao, Yifan Hao, Shuochao Yao, Dulanga Weerakoon, Huajie Shao, Shaohan Hu, Tarek Abdelzaher, Kasthuri Jayarajah, Ailing Piao |
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| Rok vydání: | 2019 |
| Předmět: |
Cooperative learning
Artificial neural network Computer science business.industry Deep learning Inference 020206 networking & telecommunications 02 engineering and technology 010501 environmental sciences 01 natural sciences Data science Scheduling (computing) Server 0202 electrical engineering electronic engineering information engineering Task analysis Artificial intelligence business Edge computing 0105 earth and related environmental sciences |
| Zdroj: | ICDCS |
| DOI: | 10.1109/icdcs.2019.00162 |
| Popis: | The paper discusses an emerging suite of machine intelligence services that are of increasing importance in the highly instrumented world of the Internet of Things (IoT). The suite, called Eugene, would offer a form of intelligent behavior (based on deep neural networks) to otherwise simple embedded devices; the clients of the service. These devices would benefit from service resources to learn from data and to perform intelligent inference, classification, prediction, and estimation tasks that they are too limited to carry out on their own. The paper discusses the taxonomy of such services and the state of implementation, as well as the various challenges entailed, including scheduling, caching (of intelligent functions), and cooperative learning. |
| Databáze: | OpenAIRE |
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