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

Preprint English OPEN
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.
  • References (70)
    70 references, page 1 of 7

    [1] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog Computing and Its Role in the Internet of Things,” in Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, ser. MCC '12. New York, NY, USA: ACM, 2012, pp. 13-16. [Online]. Available: http://doi.acm.org/10.1145/2342509.2342513

    [2] H. Kumarage, I. Khalil, A. Alabdulatif, Z. Tari, and X. Yi, “Secure Data Analytics for Cloud-Integrated Internet of Things Applications,” IEEE Cloud Computing, vol. 3, no. 2, pp. 46-56, Mar 2016.

    [3] R. Khan, S. U. Khan, R. Zaheer, and S. Khan, “Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges,” in 2012 10th International Conference on Frontiers of Information Technology, Dec. 2012, pp. 257-260.

    [4] Z. K. Zhang, M. C. Y. Cho, C. W. Wang, C. W. Hsu, C. K. Chen, and S. Shieh, “IoT Security: Ongoing Challenges and Research Opportunities,” in 2014 IEEE 7th International Conference on Service-Oriented Computing and Applications, Nov. 2014, pp. 230-234.

    [5] D. Dinu, Y. Le Corre, D. Khovratovich, L. Perrin, J. Grossscha¨dl, and A. Biryukov, “Triathlon of Lightweight Block Ciphers for the Internet of Things.” IACR Cryptology ePrint Archive, vol. 2015, p. 209, 2015.

    [6] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097- 1105.

    [7] L. Cavigelli, M. Magno, and L. Benini, “Accelerating Real-time Embedded Scene Labeling with Convolutional Networks,” in Proceedings of the 52Nd Annual Design Automation Conference, ser. DAC '15. New York, NY, USA: ACM, 2015, pp. 108:1-108:6.

    [8] R. Girshick, J. Donahue, T. Darrell, J. Malik, and U. C. Berkeley, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” in Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580-587.

    [9] G. E. Dahl, D. Yu, L. Deng, and A. Acero, “Context-Dependent PreTrained Deep Neural Networks for Large-Vocabulary Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 20, no. 1, pp. 30-42, 2012.

    [10] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” arXiv:1512.03385 [cs], Dec. 2015.

  • Similar Research Results (3)
  • Metrics
    No metrics available
Share - Bookmark