Method and system for counteracting side channel attacks

A method for counteracting side channel attack in a system comprising a neural network including processing element tiles, a machine learning (ML) power compensation unit, dynamic power digital-to-analogue converters (DACs) and static power digital-to-analogue converters (DACs), the method comprising:

• receiving, from the processing element tiles, inputs in relation to dynamic power parameters and static power parameters;
• training, a dynamic power compensation ML model associated with the ML power compensation unit, using the dynamic power parameters to form a trained dynamic power compensation ML model;
• training, a static power compensation ML model associated with the ML power compensation unit, using the static power parameters to form a trained static power compensation ML model;
• controlling, using the trained dynamic power compensation ML model, dynamic power compensation using the dynamic power DACs;
• and
• controlling, using the trained static power compensation ML model, static power compensation using the static power DACs,
• wherein the steps of training the dynamic power compensation ML model and training the static power compensation ML model are performed iteratively,
• wherein the steps of training the dynamic power compensation ML model and training the static power compensation ML model are performed offline.

A system for counteracting side channel attacks, the system comprising:

• a neural network including processing element tiles, the neural network being adapted to generate an output as a series of Multiply-Accumulate (MAC) operations;
• a first random sequence generator adapted to be used in spatial shuffling an assignment mapping of the output of the neural network to the processing element tiles;
• and
• a second random sequence generator configured to be used in temporal shuffling multiplies of the series of MAC operations in a run sequence temporally.