Soft Error-Aware Power Optimization Using Gate Sizing

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.