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.

翻译：随着人工智能模型，特别是深度神经网络的广泛应用，训练和推理过程中的能耗日益增加，这不仅引发环境方面的担忧，也促使我们寻求更节能的算法和硬件解决方案。本文针对机器学习中日益严峻的能耗问题，尤其是推理阶段的能耗问题展开研究。即使能耗的微小降低也能带来显著的节能效果，惠及用户、企业和环境。我们的方法旨在利用神经进化框架最大化人工神经网络模型的准确性，同时最小化其能耗。为此，我们将能耗纳入适应度函数中考虑。我们提出了一种新的突变策略，该策略能够随机重新引入层模块，且低功耗模块被选中的概率更高。我们引入了一种创新技术，允许在单个训练步骤中训练两个独立的模型，同时促使其中一个模型在保持相似准确率的前提下，比另一个模型更具能效。实验结果表明，该方法在未显著降低预测性能的情况下，将人工神经网络模型的能耗降低了高达29.2%。