Analysis of geometric and non-linear programming as optimization algorithms for low power VLSI circuits

In this paper, performance and accuracy of both General Geometric Programming (GGP) and non-linear programming (NLP) algorithms, for optimization of low power VLSI circuits, have been studied and compared. An optimization procedure based on GGP and logical effort method has been proposed and employed for optimization of variety of sequential logic circuits. The results were compared to the NLP algorithm of Sequential Quadratic Programming (SQP). Experiments showed that the GGP algorithm with Logical Effort method exhibits higher precision and acceptable speed compared to NLP algorithms. In fact, GGP is 9 orders of magnitude more accurate but 24x slower than NLP. However, with increasing circuit complexity the GGP does not degrade like NLP. Consequently, for complex circuits GGP is a good substitution for the speed of NLP algorithms and precision of simple LP (Linear Programming) algorithms, like Logical Effort.