Efficient Verification of a RADAR SoC Using Formal and Simulation-Based Methods
This work addresses verification bottlenecks for consumer electronics SoCs with tight time-to-market constraints, though it appears incremental as it applies existing methods to a specific case study.
The paper tackled the challenge of verifying a complex RADAR SoC for IoT and HMI applications by using a combination of formal and simulation-based methods, including ML tools, to achieve high-confidence verification sign-off.
As the demand for Internet of Things (IoT) and Human-to-Machine Interaction (HMI) increases, modern System-on-Chips (SoCs) offering such solutions are becoming increasingly complex. This intricate design poses significant challenges for verification, particularly when time-to-market is a crucial factor for consumer electronics products. This paper presents a case study based on our work to verify a complex Radio Detection And Ranging (RADAR) based SoC that performs on-chip sensing of human motion with millimetre accuracy. We leverage both formal and simulation-based methods to complement each other and achieve verification sign-off with high confidence. While employing a requirements-driven flow approach, we demonstrate the use of different verification methods to cater to multiple requirements and highlight our know-how from the project. Additionally, we used Machine Learning (ML) based methods, specifically the Xcelium ML tool from Cadence, to improve verification throughput.