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Machine Learning-based Design and Optimization of High-Speed Circuits

Machine Learning-based Design and Optimization of High-Speed Circuits

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  • More about Machine Learning-based Design and Optimization of High-Speed Circuits


Machine learning-based principles and methods are presented to accelerate communication between ICs in high-speed integrated circuits, with examples of practical designs and signal deviation compensation with linearity self-calibration. The circuit types include mixed-signal, high performance heterogeneous integrated circuits, and digital cores.

Format: Hardback
Length: 338 pages
Publication date: 03 January 2024
Publisher: Springer International Publishing AG


Machine learning-based new principles, methods of design, and optimization of high-speed integrated circuits are described in this book, which can exchange information between each other at speeds of 128/256/512 Gbps. The efficiency of these methods is proven and demonstrated on practical designs. The author introduces newly developed principles and methods to accelerate communication between ICs working in non-standard operating conditions, considering signal deviation compensation with linearity self-calibration. The observed circuit types include mixed-signal, high-performance heterogeneous integrated circuits, and digital cores.


Dimension: 235 x 155 (mm)
ISBN-13: 9783031507137
Edition number: 1st ed. 2024

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