  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.
