Zeus: An Open-Source Optimization Framework for Energy-Efficient Deep Learning Models

The University of Michigan researchers have developed an open-source optimization framework called Zeus to address the energy consumption problem in deep learning models. As the use of larger models with more parameters becomes more popular, the demand for energy to train these models also increases. Zeus aims to solve this problem by finding the optimal balance between energy consumption and training speed during the training process, without the need for any hardware changes or new infrastructure.

How Zeus Works

Zeus uses two software knobs: the GPU power limit and the batch size parameter of the deep learning model. The GPU power limit controls the amount of power consumed by the GPU, while the batch size parameter determines how many samples are processed before updating the model’s representation of the data’s relationships. By adjusting these parameters in real-time, Zeus minimizes energy consumption while minimizing the impact on training time.

Compatibility and Additional Features

Zeus is designed to work with various machine learning tasks and GPUs without requiring any changes to the hardware or infrastructure. The researchers have also developed complementary software called Chase, which prioritizes speed when low-carbon energy is available and efficiency during peak times to reduce the carbon footprint of deep neural network (DNN) training.

The Significance of Zeus and Chase

Zeus and Chase are essential in addressing the energy consumption problem of deep learning models. By reducing the energy demand of these models, researchers can minimize the impact of artificial intelligence on the environment and promote sustainable practices in the field. These optimization tools do not sacrifice accuracy, as demonstrated by the significant energy savings achieved without affecting training time.

Overall, Zeus is an open-source optimization framework that aims to reduce the energy consumption of deep learning models. It achieves this by finding the optimal balance between energy consumption and training speed. Zeus is compatible with various machine learning tasks and can be used with different GPUs. The complementary software Chase further reduces the carbon footprint of deep neural network training. These developments contribute to sustainable practices in artificial intelligence and mitigate its environmental impact.

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