causal probabilistic input dependency learning for switching model in vlsi circuits
Switching model captures the data-driven uncertainty in logic circuits in a comprehensive probabilistic framework. Switching is a critical factor that influences dynamic, active leakage power, coupling noises in CMOS implementations. In this work, we model the input-space by a causal graphical probabilistic model that encapsulates the dependencies in inputs in a compact, minimal fashion and also allows for instantiations of the vector-space that closely match the underlying dependencies, with the constraint that the reduced vector-space captures the dependencies in the larger dataset accu-rately. Results on ISCAS benchmark show that average error is limited to 1.8\% while we achieve a compaction ratio of 300.
probabilistic learning cross-talk estimation bayesian networks vector compaction power estimation