A ThreshoId-ImpIementation-Based Neural-Network Accelerator Securing Model Parameters and Inputs Against Power Side-Channel Attacks

Maji, S and Banerjee, U and Fuller, SH and Chandrakasan, AP (2022) A ThreshoId-ImpIementation-Based Neural-Network Accelerator Securing Model Parameters and Inputs Against Power Side-Channel Attacks. In: IEEE International Solid-State Circuits Conference, ISSCC 2022, 20-26 February 2022, San Francisco, pp. 518-520.

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Abstract

Neural network (NN) hardware accelerators are being widely deployed on low-power loT nodes for energy-efficient decision making. Embedded NN implementations can use locally stored proprietary models, and may operate over private inputs (e.g., health monitors with patient-specific biomedical classifiers 6), which must not be disclosed. Side-channel attacks (SCA) are a major concern in embedded systems where physical access to the operating hardware can allow attackers to recover secret data by exploiting information leakage through power consumption, timing and electromagnetic emissions 1, 7, 8. As shown in Fig. 34.3.1, SCA on embedded NN implementations can reveal the model parameters 9 as well as the inputs 10. To address these concerns, we present an energy - efficient ASlC solution for protecting both the model parameters and the input data against power-based SCA. © 2022 IEEE.

Item Type:	Conference Proceedings
Publication:	Digest of Technical Papers - IEEE International Solid-State Circuits Conference
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Decision making; Embedded systems; Low power electronics; Side channel attack, Embedded neural networks; Energy efficient; Hardware accelerators; Low Power; Model inputs; Modeling parameters; Neural network hardware; Neural-networks; Power; Side-channel attacks, Energy efficiency
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	19 May 2022 05:44
Last Modified:	19 May 2022 05:44
URI:	https://eprints.iisc.ac.in/id/eprint/72015

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