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 accurately. Results on ISCAS benchmark show that average error is

• Type: Conference
• Year: 2005
• Where: GLVLSI
• Authors: Nirmal Ramalingam, Sanjukta Bhanja
• Keywords: Active Leakage Power, Comprehensive Probabilistic Framework, GLVLSI 2005, Graphical Probabilistic Model, VLSI