Profiling the Energy Consumption of Secure Neural Network Inference

Le résumé fourni par la source

Secure neural network inference (SNNI) enables the use of deep neural networks in scenarios involving multiple stakeholders, protecting the confidentiality of client data and of the neural network’s parameters. The cryptographic techniques used introduce high computational overhead, leading to significant energy consumption. Reducing the energy consumption of SNNI is thus an important objective. A prerequisite for energy optimization is the ability to profile the energy consumption of SNNI. However, this is challenging due to the complexity of the cryptographic techniques, neural networks, and technical setup involved. This paper is the first to propose an energy profiling approach for SNNI. Our approach measures the energy consumption for securely processing individual layers of the neural network, thus providing fine-grained insights into the energy profile of SNNI. We evaluate our approach using the ResNet50 neural network and the Cheetah SNNI framework. Our results show that we can reliably measure the energy consumption of individual layers. By introducing short periods of inactivity between layers to disentangle them, we achieve high correlation between execution time and energy consumption, suggesting that, under appropriate conditions, execution time may be used as a proxy for energy consumption. Our approach and insights can foster the design of more energy-efficient SNNI protocols.

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