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

Autor: Pasquale Davide Schiavone, Robert Schilling, Igor Loi, Germain Haugou, Frank K. Gurkaynak, Luca Benini, Francesco Conti, Davide Rossi, Michael Gautschi, Michael Muehlberghuber, Antonio Pullini, Stefan Mangard
Přispěvatelé: 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
Jazyk: angličtina
Předmět:
FOS: Computer and information sciences
Computer Science - Cryptography and Security
Computer science
neural network
Data security
02 engineering and technology
Encryption
Machine Learning (cs.LG)
Software
Hardware Architecture (cs.AR)
0202 electrical engineering
electronic engineering
information engineering
System on a chip
Neural and Evolutionary Computing (cs.NE)
Computer architecture
Electrical and Electronic Engineering
Computer Science - Hardware Architecture
low-power electronic
encryption
business.industry
feature extraction
020208 electrical & electronic engineering
Computer Science - Neural and Evolutionary Computing
Pipeline (software)
approximate computing
020202 computer hardware & architecture
parallel architecture
Computer Science - Learning
Analytics
Embedded system
Data analysis
Internet of Thing
business
Cryptography and Security (cs.CR)
Efficient energy use
Zdroj: IEEE Transactions on Circuits and Systems I: Regular Papers
ISSN: 1558-0806
1549-8328
DOI: 10.1109/tcsi.2017.2698019
Popis: Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.
15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Papers
Databáze: OpenAIRE
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