An efficient algorithm for statistical circuit optimization using lagrangian relaxation

Due to the technology scaling down, process variation has become a crucial challenge on both interconnect delay and reliability. To handle the process variation, statistical optimization has emerged as a popular technique for yield improvement. As a relatively new technique, second-order conic programming (SOCP) has recently attracted very much attention in the literature for statistical circuit optimization. However, we observe significant limitations of SOCP in its flexibility, accuracy, and scalability for statistical circuit optimization, especially when interconnects are considered. We thus present in this paper an effective and efficient alternative for multi-constrained statistical circuit optimization by both gate and wire sizing using Lagrangian relaxation (LR). Compared with SOCP, experimental results show that our LR-based algorithm can achieve much better solution quality by reducing 21% area and obtain 560X speed-up over SOCP.