Scalable Deep Learning for Industry 4.0: Speedup with Distributed Deep Learning and Environmental Sustainability Considerations

This paper presents a solution for leveraging HighPerformance Computing (HPC) infrastructures, and investigates the
integration of distributed deep learning (DDL) techniques to address
Industry 4.0 challenges across three distinct applications: intrusion detection with multi-layer perceptron, defect identification with convolutional
neural networks, and predictive maintenance with recurrent neural network. Experimental results, underscore the scalability and efficiency of
the proposed DDL approach. Notably, computations are accelerated by
up to 46 times.
In addition to performance metrics, this research places significant emphasis on environmental sustainability. Detailed examination of
energy consumption patterns on the HPC infrastructure aims to minimize the carbon footprint associated with deep learning processes. This
dual focus on efficiency and sustainability positions the approach as a
holistic and responsible solution for Industry 4.0 applications.
The practical insights enhance the efficiency of deploying DDL in
HPC infrastructure. Additionally, they highlight the significance of ecofriendly AI practices for ethical and environmentally sustainable technological progress.