Power Optimization of Technology-Dependent Circuits Based on Symbolic Computation of Logic Implications

This paper presents a novel approach to the problem of optimizing combinational circuits for low power. The method is inspired by the fact that power analysis performed on a technology mapped network gives more realistic estimates than it would at the technology-independent level. After each node's switching activity in the circuit is determined, high-power nodes are eliminated through redundancy addition and removal. To do so, the nodes are sorted according to their switching activity, they are considered one at a time, and learning is used to identify direct and indirect logic implications inside the network. These logic implications are exploited to add gates and connections to the circuit; this may help in eliminating high-power dissipating nodes, thus reducing the total switching activity and power dissipation of the entire circuit. The process is iterative; each iteration starts with a different target node. The end result is a circuit with a decreased switching power. Besides the general optimization algorithm, we propose a new BDD-based method for computing satisfiability and observability implications in a logic network; futhermore, we present heuristic techniques to add and remove redundancy at the technology-dependent level, that is, restructure the logic in selected places without destroying the topology of the mapped circuit. Experimental results show the effectiveness of the proposed technique. On average, power is reduced by 34%, and up to a 64% reduction of power is possible, with a negligible increase in the circuit delay.