Learn to Tune: Robust Performance Tuning in Post-Silicon Validation

Abstract

Post-silicon validation is a crucial yet challenging problem primarily due to the increasing complexity of the semi-conductor value chain. Existing techniques cannot keep up with the rapid increase in the complexity of designs. Therefore, post-silicon validation is becoming an expensive bottleneck. Robust performance tuning is relevant to compensate impacts of process variations and non-ideal design implementations. We propose a novel approach based on Deep Reinforcement Learning and Learn to Optimize. The method automatically learns flexible tuning strategies tailored to specific circuits. Additionally, it addresses high-dimensional tuning tasks, including mixed data types and dependencies, e.g., on operating conditions. In this work, we introduce Learn to Tune and demonstrate its appealing properties in post-silicon validation, e.g., lower computational cost or faster time-to-optimize, allowing a more efficient adaption of the tuning to changing tuning conditions than classical methods.