Optimization techniques for high-performance digital circuits

• Authors: Chandu Visweswariah
• Venue: ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
• Year: 1997

The relentless push for high performance in custom digital circuits has led to renewed emphasis on circuit optimization or tuning. The parameters of the optimization are typically transistor and interconnect sizes. The design metrics are not just delay, transition times, power and area, but also signal integrity and manufacturability. This tutorial paper discusses some of the recently proposed methods of circuit optimization, with an emphasis on practical application and methodology impact. Circuit optimization techniques fall into three broad categories. The first is dynamic tuning, based on time-domain simulation of the underlying circuit, typically combined with adjoint sensitivity computation. These methods are accurate but require the specification of input signals, and are best applied to small data-flow circuits and "cross-sections" of larger circuits. Efficient sensitivity computation renders feasible the tuning of circuits with a few thousand transistors. Second, static tuners employ static timing analysis to evaluate the performance of the circuit. All paths through the logic are simultaneously tuned, and no input vectors are required. Large control macros are best tuned by these methods. However, in the context of deep submicron custom design, the inaccuracy of the delay models employed by these methods often limits their utility. Aggressive dynamic or static tuning can push a circuit into a precipitous corner of the manufacturing process space, which is a problem addressed by the third class of circuit optimization tools, statistical tuners. Statistical techniques are used to enhance manufacturability or maximize yield. In addition to surveying the above techniques, topics such as the use of state-of-the-art nonlinear optimization methods and special considerations for interconnect sizing, clock tree optimization and noise-aware tuning will be briefly considered.