The sustained push toward smaller and smaller technology sizes has reached a point where device reliability has moved to the forefront of concerns for next-generation designs. Silicon failure mechanisms, such as transistor wearout and manufacturing defects, are a growing challenge that threatens the yield and product lifetime of future systems. In this paper we introduce the BulletProof pipeline, the first ultra low-cost mechanism to protect a microprocessor pipeline and on-chip memory system from silicon defects. To achieve this goal we combine area-frugal on-line testing techniques and system-level checkpointing to provide the same guarantees of reliability found in traditional solutions, but at much lower cost. Our approach utilizes a microarchitectural checkpointing mechanism which creates coarse-grained epochs of execution, during which distributed on-line built in self-test (BIST) mechanisms validate the integrity of the underlying hardware. In case a failure is detected, we rely on the natural redundancy of instructionlevel parallel processors to repair the system so that it can still operate in a degraded performance mode. Using detailed circuit-level and architectural simulation, we find that our approach provides very high coverage of silicon defects (89%) with little area cost (5.8%). In addition, when a defect occurs, the subsequent degraded mode of operation was found to have only moderate performance impacts, (from 4% to 18% slowdown).

Constrained random simulation is a widespread technique used to perform functional verification on complex digital designs, because it can generate simulation vectors at a very high rate. However, the generation of high-coverage tests remains a major challenge even in light of this high performance. In this paper we present Guido, a hybrid verification software that uses formal verification techniques to guide the simulation towards a verification goal. Guido is novel in that 1) it guides the simulation by means of a distance function derived from the circuit structure, and 2) it has a trace sequence controller that monitors and controls the direction of the simulation by striking a balance between random chance and controlled hill-climbing. We present experimental results indicating that Guido can tackle complex designs, including a picoJava microprocessor, and reach a verification goal in far fewer simulation cycles than random