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

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Conti, Francesco ; Schilling, Robert ; Schiavone, Pasquale Davide ; Pullini, Antonio ; Rossi, Davide ; Gürkaynak, Frank Kagan ; Muehlberghuber, Michael ; Gautschi, Michael ; Loi, Igor ; Haugou, Germain ; Mangard, Stefan ; Benini, Luca (2016)
  • Related identifiers: doi: 10.1109/TCSI.2017.2698019
  • Subject: Computer Science - Hardware Architecture | Computer Science - Neural and Evolutionary Computing | Computer Science - Learning | Computer Science - Cryptography and Security

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.
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