GPU-based Hybrid Parallel Logic Simulation for Scan Patterns

GPGPU, general-purpose computing on graphics processing units, has been witnessed growing from a niche to a mainstream computing paradigm in the last decade. It is widely deployed in machine learning, artificial intelligence, and many scientific applications. In this article, we study gate-level logic simulation for scan test patterns, one of the key algorithmic components for test generation, fault grading and design rule check. We are exploring if GPGPU can deliver scalable performance speedup as promised by its massive parallelism. We discuss the limitations of the state of art work. With GPUs' architectural features in mind, a novel algorithm of hybrid race-tolerant parallel logic simulation is proposed to unleash its immense power of parallelization. Its unique integration of oblivious simulation and event-driven simulation leads to a scalable realization of parallel logic simulation for scan patterns. For the first time in the literature, it is demonstrated that a modest GPU can handle an industrial design of over twenty million gates with excellent performance scalability.