Session

DescriptionThis session provides insights into innovative methodologies addressing key challenges in timing analysis, prediction, and optimization. Topics include ultra-fast statistical static timing analysis, machine learning-driven timing prediction, and advanced characterization techniques. Additionally, the session explores a fast clock skew scheduling algorithm for improved timing and an approach to mitigating security risks posed by glitch-induced transitions in cryptographic hardware.