Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Fast and exact simultaneous gate and wire sizing by Lagrangian relaxation
Proceedings of the 1998 IEEE/ACM international conference on Computer-aided design
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Impact of interconnect variations on the clock skew of a gigahertz microprocessor
Proceedings of the 37th Annual Design Automation Conference
Analysis and optimization of thermal issues in high-performance VLSI
Proceedings of the 2001 international symposium on Physical design
Convex Optimization
Novel sizing algorithm for yield improvement under process variation in nanometer technology
Proceedings of the 41st annual Design Automation Conference
A New Statistical Optimization Algorithm for Gate Sizing
ICCD '04 Proceedings of the IEEE International Conference on Computer Design
An efficient algorithm for statistical minimization of total power under timing yield constraints
Proceedings of the 42nd annual Design Automation Conference
Statistical analysis and optimization in the presence of gate and interconnect delay variations
Proceedings of the 2006 international workshop on System-level interconnect prediction
Statistical circuit optimization considering device andinterconnect process variations
Proceedings of the 2007 international workshop on System level interconnect prediction
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Proceedings of the 2009 international symposium on Physical design
| Hi-index | 0.00 |
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.