Towards Physical Plausibility in Neuroevolution Systems
Gabriel Cortês Nuno Lourenço Penousal Machado
The increasing usage of Artificial Intelligence (AI) models, especially Deep
Neural Networks (DNNs), is increasing the power consumption during training and
inference, posing environmental concerns and driving the need for more
energy-efficient algorithms and hardware solutions. This work addresses the
growing energy consumption problem in Machine Learning (ML), particularly
during the inference phase. Even a slight reduction in power usage can lead to
significant energy savings, benefiting users, companies, and the environment.
Our approach focuses on maximizing the accuracy of Artificial Neural Network
(ANN) models using a neuroevolutionary framework whilst minimizing their power
consumption. To do so, power consumption is considered in the fitness function.
We introduce a new mutation strategy that stochastically reintroduces modules
of layers, with power-efficient modules having a higher chance of being chosen.
We introduce a novel technique that allows training two separate models in a
single training step whilst promoting one of them to be more power efficient
than the other while maintaining similar accuracy. The results demonstrate a
reduction in power consumption of ANN models by up to 29.2% without a
significant decrease in predictive performance.