Biblio

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.

The detectability of malicious circuitry on FPGAs with varying placement properties yet has to be investigated. The authors utilize a Xilinx Virtex-II Pro target platform in order to insert a sequential denial-of-service Trojan into an existing AES design by manipulating a Xilinx-specific, intermediate file format prior to the bitstream generation. Thereby, there is no need for an attacker to acquire access to the hardware description language representation of a potential target architecture. Using a side-channel analysis setup for electromagnetic emanation (EM) measurements, they evaluate the detectability of different Trojan designs with varying location and logic distribution properties. The authors successfully distinguish the malicious from the genuine designs and provide information on how the location and distribution properties of the Trojan logic affect its detectability. To the best of their knowledge, this has been the first practically conducted Trojan detection using localized EM measurements.