Optimization of Chip Design Using Machine Learning Techniques
DOI:
https://doi.org/10.5281/zenodo.13845111ARK:
https://n2t.net/ark:/40704/JIEAS.v2n5a05References:
27Keywords:
Chip Design, Machine Learning, Optimization, Performance, Power ConsumptionAbstract
Complex chip designs necessitate innovative strategies in order to accelerate and streamline their design processes. This paper investigates the use of machine learning (ML) techniques in chip design, with emphasis placed on optimizing strategies that increase performance while decreasing power consumption and increasing design efficiency. By reviewing recent advances and case studies, we demonstrate how machine learning (ML) has the power to transform traditional design methodologies. Furthermore, we explore various ML algorithms, their uses at various stages in chip design processes, as well as any challenges experienced during implementation. Findings indicate that using machine learning (ML) to expedite design can significantly streamline the design process and speed development cycles, as well as optimize resource usage more effectively. This paper seeks to give an extensive overview of current ML practices used for chip design as well as future research directions that aim at improving design practices via innovative technologies.
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