Gate sizing consists of choosing for each node of a mapped network a gate implementation in the library so that some cost function is optimized under some constraints. It has a significant impact on the delay, power dissipation, and area of the final circuit. This paper compares five gate sizing algorithms targeting discrete, non-linear, non-unimodal, constrained optimization. The goal is to overcome the non-linearity and nonunimodality of the delay and the power to achieve good quality results within a reasonable CPU time, e.g., handling a 10000 node network in 2 hours. We compare the five algorithms on constraint free delay optimization and delay constrained power optimization, and show that one method is superior to the others. 1 Introduction Early work on gate sizing targeting area/delay optimization can be found in [20, 12]. Using a RC delay model, TILOS [8] expresses the delay and area as posynomials. Geometric programming or heuristics based greedy approaches can be used to so...

The problem of power-delay tradeoffs in transistor sizing is examined using a nonlinear optimization formulation. Both the dynamic and the short-circuit power are considered, and a new modeling technique is used to calculate the short-circuit power. The notion of transition density is used, with an enhancement that considers the effect of gate delays on the transition density. When the short-circuit power is neglected, the minimum power circuit is identical to the minimum area circuit. However, under our more realistic models, our experimental results on several circuits show that the minimum power circuit is not necessarily the same as the minimum area circuit.

A new performance and area optimization algorithm for complex VLSI systems is presented. It is widely believed within the VLSI CAD community that the relationship between delay and silicon area of a VLSI chip is convex. This conclusion is based on a simplified linear RC model to predict gate delays. In the proposed optimization algorithm, a nonlinear, non-RC based transistor delay model was used which resulted in a non-convex relationship between the delay and the silicon area of a VLSI chip. Genetic algorithms are better suited for discrete, non-convex, non-linear optimization problems than traditional calculus-based algorithms. By using the genetic algorithms in the performance and area optimization, we are able to find the optimal values for both delay and silicon area for the ISCAS benchmark circuits. Key Words: Area and Performance optimization; Transistor Sizing; Genetic algorithms 1 Introduction The techniques for performance and area optimization of VLSI systems can be divi...

Abstract. Power consumption has emerged as the premier and most constraining aspect in modern microprocessor and application specific designs. Gate sizing has been shown to be one of the most effective methods for power (and area) reduction in CMOS digital circuits. Recently, as the feature size of logic gates (and transistors) is becoming smaller and smaller, the effect of soft error rates caused by single event upsets (SEU) is becoming exponentially greater. As a consequence of technology feature size reduction, the SEU rate for typical microprocessor logic at the sea level will go from one in hundred years to one every minute. Unfortunately, the gate sizing requirements of power reduction and resiliency against SEU can be contradictory. 1) We consider the effects of gate sizing on SEU and incorporate the relationship between power reduction and SEU resiliency to develop a new method for power optimization under SEU constraints. 2) Although a non-linear programming approach is a more obvious solution, we propose a convex programming formulation that can be solved efficiently. 3) Many of the optimal existing techniques for gate sizing deal with an exponential number of paths in the circuit, we prove that it is sufficient to consider a linear number of constraints. As an important preprocessing step we apply statistical modeling and validation techniques to quantify the impact of fault masking on the SEU rate. We evaluate the effectiveness of our methodology on ISCAS benchmarks and show that error rates can be reduced by a factor of 100 % to 200 % while, on average, the power saving is simultaneously decreased by less than 7 % to 12 % respectively, compared to the optimal power saving with no error rate constraints. 1