Machine Learning Techniques for VLSI Chip Design 1st Edition

Machine Learning Techniques for VLSI Chip Design — 1st Edition by Abhishek Kumar, Suman Lata Tripathi, and K. Srinivasa Rao

Discover a practical, forward-looking guide that bridges modern machine learning with real-world VLSI chip design. This book opens with a compelling overview of why ML is transforming layout, placement, timing closure, power optimization and yield prediction, capturing the attention of engineers, researchers and advanced students across semiconductor hubs from India and Taiwan to the USA and Europe.

Dive into clear, applied explanations of relevant algorithms — from supervised and unsupervised learning to deep learning and reinforcement strategies — and learn how they map to classic VLSI challenges. The authors present approachable methodology for feature selection, model evaluation, interpretability and deployment, supported by case-driven examples so readers can move from theory to prototype quickly. Practical insights on data pipelines for chip design and strategies to reduce design cycles make the material invaluable for CAD engineers and design teams alike.

Whether you’re aiming to accelerate timing closure, improve power efficiency, or enhance manufacturing yield, this edition offers actionable techniques that boost productivity and competitiveness in global semiconductor markets. The book’s balanced mix of conceptual clarity and hands-on guidance fosters confidence to implement ML solutions on real design flows.

Ideal for professionals, graduate students, and engineering teams seeking an authoritative reference, Machine Learning Techniques for VLSI Chip Design (1st Edition) is the essential resource to modernize your design toolkit. Order now to start integrating intelligent, data-driven approaches into your chip design workflow.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.