The study explores the complex relationship between dataset size, network structure, and energy consumption in deep neural networks. It introduces the BUTTER-E dataset, highlighting the surprising non-linear relationship between energy efficiency and network design. The analysis uncovers the critical role of cache-considerate algorithm development in achieving greater energy efficiency.
Large-scale neural networks contribute significantly to rising energy consumption in computing systems. Despite advancements in chip technology, AI's exponential growth leads to increased energy usage. The study proposes practical guidance for creating more energy-efficient neural networks and promoting sustainable AI.
Key findings include the impact of hyperparameter choice on energy efficiency across different architectures and counter-intuitive results related to hardware-mediated interactions. The research suggests a combined approach to developing energy-efficient architectures, algorithms, and hardware design.
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by Charles Edis... pada arxiv.org 03-14-2024
https://arxiv.org/pdf/2403.08151.pdfPertanyaan yang Lebih Dalam