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

By Francesco Conti, R. Schilling, Pasquale Davide Schiavone, Antonio Pullini, Davide Rossi, Frank Kagan Gürkaynak, M. Muehlberghuber, Michael Gautschi, Igor Loi, Germain Haugou and 2 more. 2016

122 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Hardware and Architecture

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Citations in the corpus

A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones (2018, background): “Nano-UAVs could also perform advanced onboard analytics, to pre-select essential information before transmitting it to centralized servers [3].”
The Internet of Things, Fog and Cloud Continuum: Integration and Challenges (2018, background): “The former consists of various energy-eﬃcient architectural features that may include techniques for voltage scaling or cooling [62, 63, 64, 65, 66].”
Target following on nano-scale Unmanned Aerial Vehicles (2017, methodology): “In this section we evaluate the power consumption and the computational load of the proposed solution for both the STM32 MCU and a PULP architecture [17].”