<ul><li>Puneet Gupta introduces a machine learning-enhanced greedy algorithm to resolve hold time violations in advanced SoC designs.</li><li>Conventional hold-fixing approaches focus on endpoint-based delay cell insertion, but Gupta's methodology adopts a system-level perspective by identifying shared paths and implementing coordinated fixes across multiple violations simultaneously.</li><li>By leveraging machine learning algorithms, the methodology minimizes disruption to critical paths, optimizes delay insertion strategies, and reduces buffer count by 30-40%.</li><li>Gupta's methodology demonstrates significant improvements in power efficiency, area savings, and reduction in timing closure iterations, making it a valuable advancement in semiconductor design.</li></ul>

Optimizing Chip Design with Machine Learning-Driven Greedy Algorithms

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