Towards Physical Plausibility in Neuroevolution Systems

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 …

algorithms artificial artificial intelligence concerns consumption cs.ai cs.lg cs.ne driving energy environmental hardware inference intelligence machine machine learning networks neural networks neuroevolution power power consumption solutions systems training usage work