Circuit Optimization Techniques to Mitigate the Effects of Soft Errors in Combinational Logic

摘要

Soft errors in combinational logic circuits are emerging as a significant reliability problem for VLSI designs. Technology scaling trends indicate that the soft error rates (SER) of logic circuits will be dominant factor for future technology generations. SER mitigation in logic can be accomplished by optimizing either the gates inside a logic block or the flipflops present on the block boundaries. We present novel circuit optimization techniques that target these elements separately as well as in unison to reduce the SER of combinational logic circuits. First, we describe the construction of a new class of flip-flop variants that leverage the effect of temporal masking by selectively increasing the length of the latching window thereby preventing faulty transients from being registered. In contrast to previous flip-flop designs that rely on logic duplication and complicated circuit design styles, the new variants are redesigned from the library flip-flop using efficient transistor sizing. We then propose a flip-flop selection method that uses slack information at each primary output node to determine the flip-flop configuration that produces maximum SER savings. Next, we propose a gate sizing algorithm that trades off SER reduction and area overhead. This approach first computes bounds on the maximum achievable SER reduction by resizing a gate. This bound is then used to prune the circuit graph, arriving at a smaller set of candidate gates on which we perform incremental sensitivity computations to determine the gates that are the largest contributors to circuit SER. Third, we propose a unified, co-optimization approach combining flip-flop selection with the gate sizing algorithm. The joint optimization algorithm produces larger SER reductions while incurring smaller circuit overhead than either technique taken in isolation. Experimental results on a variety of benchmarks show average SER reductions of 10.7X with gate sizing, 5.7X with flip-flop assignment, and 30.1X for the combined optimization approach, with no delay penalties and area overheads within 5-6%. The runtimes for the optimization algorithms are on the order of 1-3 minutes.