B.TECH. IN ELECTRONICS ENGINEERING (VLSI DESIGN AND TECHNOLOGY)electivetheory
MACHINE LEARNING FOR VLSI DESIGN AUTOMATION
ECE 4421
Syllabus
- 01VLSI physical design
- 02Design automation tools
- 03Data structures and algorithms
- 04Graph theory and computational complexity
- 05Traditional CAD algorithms vs machine learning approaches
- 06Combinatorial optimization tasks (partitioning, floor planning, placement, routing, synthesis, testing)
- 07Evolutionary algorithms (Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing)
- 08ML models (K-Means, K-Nearest Neighbors, Decision Trees, Random Forests)
- 09Chip partitioning, congestion prediction, routability-driven placement
- 10ML-based physical verification and mask synthesis
- 11Layout feature extraction, hotspot detection, optical proximity correction
- 12ML techniques in testing, manufacturing, yield prediction, and failure modelling
- 13Statistical methods and Gaussian process-based models for high-volume production
References
- Elfadel, Ibrahim M., Duane S. Boning, and Xin Li, eds. Machine learning in VLSI computer-aided design. Cham: Springer, 2019.
- Kumar, Abhishek, Suman Lata Tripathi, and K. Srinivasa Rao, eds. Machine Learning Techniques for VLSI Chip Design. John Wiley & Sons, 2023.
- Saini, Sandeep, Kusum Lata, and G. R. Sinha, eds. VLSI and Hardware Implementations Using Modern Machine Learning Methods. CRC Press, 2021.
- N.A. Sherwani, "Algorithms for VLSI Physical Design Automation ", 1999.
- NPTEL: CAD for VLSI Design https://archive.nptel.ac.in/courses/106/106/106106088/
Credits Structure
3Lecture
0Tutorial
0Practical
3Total