2020 IEEE Darlington Award of Transactions on Circuits and Systems (TCAS-I) to F. Conti, L. Benini and D. Rossi

Francesco Conti (first author), Luca Benini and Davide Rossi have received the 2020 Darlington Award for the paper: An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Pubblicato: 20 November 2020 | Award

2020 Darlington Award of IEEE CAS Society

The paper is the result of a partnership of University of Bologna, Integrated Systems Laboratory of ETH Zürich, Graz University of Technology.

"An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics" IEEE Transactions on Circuits and Systems I: Regular Papers (Volume 64, Issue 9, Sept. 2017)

Authors: Francesco Conti, Robert Schilling, Pasquale Davide Schiavone, Antoni Pullini, Davide Rossi, Frank K. Gürkaynak, Michael Muehlberghuber, Michael Gautschi, Igor Loi, Germain Haugou, Stefan Mangard, Luca Benini.


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 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 (SoC) 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 65-nm 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 convolutional neural network (CNN) consuming 3.16pJ per equivalent reduced instruction set computer operation, local CNN-based face detection with secured remote recognition in 5.74pJ/op, and seizure detection with encrypted data collection from electroencephalogram within 12.7pJ/op.