A SystemC SNN model for power trace generation

Abstract

Power analysis can be used to retrieve key information as secure systems leak data-dependent information over side channels. A proposed solution to break the correlation between side channel information and secret information was to replace a vulnerable part of the cryptography implementation with a neural network. This uses the inherent properties of a neural network to disrupt the correlation by breaking the linear power characteristics assumed by leakage models. To test this neural network without physically creating a hardware implementation a simulation must be performed that provides both the data and the power information. Currently neural network simulators do not generate a power trace and analog circuit simulators generate more information traces than required increasing the simulation time. This thesis describes the creation of a complete SystemC spiking neural network model that generates both data and power information. The information generated by this model was compared and verified with results acquired by the Cadence Spectre  analog circuit simulation platform. The results indicate that the created SystemC SNN model works and generates comparable data and power traces as the Spectre simulator.