Scheduling using Convolutional Neural Network in GPU environment

• Autorzy: Świtalski, Piotr , Siwiak, Karolina
• Języki publikacji: EN

Abstrakty

EN

Graphics processing units (GPU) have become the foundation of artificial intelligence. Machine learning was slow, inaccurate, and inadequate for many of today’s applications. The inclusion and utilization of GPUs made a remarkable difference in large neural networks. The numerous core processors on a GPU allow machine learning engineers to train complex models using many files relatively quickly. The ability to rapidly perform multiple computations in parallel is what makes them so effective; with a powerful processor, the model can make statistical predictions about very large amounts of data. GPUs are widely used in machine learning because they offer more power and speed than CPUs. In this paper, we show the use of GPU for solving a scheduling problem. The results show that this idea is useful, especially for large optimization problems.

Słowa kluczowe

EN

Job Shop Scheduling Problem; Convolutional Neural Network; optimization; genetic algorithm

• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2024
• Tom: vol. 1 (30)
• Strony: 81--96
• Opis fizyczny: Bibliogr. 17 poz., rys., tab., wykr.

Twórcy

autor

Świtalski, Piotr

• University of Siedlce, Faculty of Exact and Natural Sciences, Institute of Computer Science,

autor

Siwiak, Karolina

• University of Siedlce, Faculty of Exact and Natural Sciences, Institute of Computer Science,

Bibliografia

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Identyfikatory

DOI

10.34739/si.2024.30.06