Titlebook: Machine Learning Applications in Electronic Design Automation; Haoxing Ren,Jiang Hu Book 2022 The Editor(s) (if applicable) and The Author

寬度

CARK

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Net-Based Machine Learning-Aided Approaches for Timing and Crosstalk Predictionethods either too slow or very inaccurate. Thanks to their strong knowledge extraction and reuse capability, machine learning (ML) techniques have been adopted to improve the predictability of timing and crosstalk effects at different design stages. Many of these works develop net-based models, whos

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新奇

Deep Learning for Analyzing Power Delivery Networks and Thermal Networksal intensive step is a critical part of the IC design process and has been a significant computational bottleneck for electronic design automation. Machine learning techniques can efficiently solve these problems by performing fast and accurate analysis and optimization. This chapter presents ML met

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Machine Learning for Testability Predictiongn. Recent advances in machine learning provide new methodologies to enhance various design stages in the design cycle. This chapter will discuss typical machine learning approaches for testability measurements, which focuses on a set of testability-related prediction problems in both component leve

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RL for Placement and Partitioningrial optimization problem. Next, this chapter delves briefly into the six decades of prior work on this important topic. The heart of the chapter is an overview of deep RL, a primer on how to formulate chip placement as a deep RL problem, and a detailed description of a recent RL-based approach to c

猛烈責(zé)罵

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Circuit Optimization for 2D and 3D ICs with Machine Learningspeedups and dramatic advances in the design process. This chapter presents how traditional physical design algorithms and their extensive portfolio of design settings can be replaced or enhanced with machine learning and a data-driven philosophy. Indeed, using powerful machine learning methods can