Dynamic Refinement of Hardware Assertion Checkers

• Main Authors: Witharana, Hasini, Sanjaya, Sahan, Mishra, Prabhat
• Format: Conference Proceeding
• Language: English
• Subjects: Hardware; Machine learning; Power demand; Predictive models; Runtime; Simulated annealing; System-on-chip
• ISSN: 1558-1101
• DOI: 10.23919/DATE56975.2023.10137306

Post-silicon validation is a vital step in System-on-Chip (SoC) design cycle. A major challenge in post-silicon validation is the limited observability of internal signal states using trace buffers. Hardware assertions are promising to improve the observability during post-silicon debug. Unfortunately, we cannot synthesize thousands (or millions) of pre-silicon assertions as hardware checkers (coverage monitors) due to hardware overhead constraints. Prior efforts considered synthesis of a small set of checkers based on design constraints. However, these design constraints can change dynamically during the device lifetime due to changes in use-case scenarios as well as input variations. In this paper, we explore dynamic refinement of hardware checkers based on changing design constraints. Specifically, we propose a cost-based assertion selection framework that utilizes non-linear optimization as well as machine learning. Experimental results demonstrate that our machine learning model can accurately predict area (less than 5% error) and power consumption (less than 3% error) of hardware checkers at runtime. This accurate prediction enables close-to-optimal dynamic refinement of checkers based on design constraints.