An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Article, Preprint English OPEN
Conti, Francesco; Schilling, Robert; Schiavone, Pasquale Davide; Pullini, Antonio; Rossi, Davide; Gurkaynak, Frank Kagan; Muehlberghuber, Michael; Gautschi, Michael; Loi, Igor; Haugou, Germain; Mangard, Stefan; Benini, Luca;
(2016)
  • Journal: IEEE Transactions on Circuits and Systems I: Regular Papers, volume 64, issue 9, pages 2,481-2,494 (issn: 1549-8328, eissn: 1558-0806)
  • Publisher copyright policies & self-archiving
  • Identifiers: doi: 10.1109/TCSI.2017.2698019
  • Subject: approximate computing | parallel architectures | neural networks | Computer Science - Learning | encryption | Internet of Things | feature extraction | Computer architecture | Electrical and Electronic Engineering | Computer Science - Hardware Architecture | low-power electronics | Computer Science - Neural and Evolutionary Computing | Computer Science - Cryptography and Security
Near-sensor data analytics is a promising direction for internet-of-things endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data are stored or sent over the network at various stages of ... View more
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