GPGPU-Based ATPG System: Myth or Reality?

General-purpose computing on graphics processing units (GPGPUs) is a programming model that uses graphics cards to perform computations traditionally done by CPU. It began to become practical with the advent of programmable shaders and floating-point support on GPU in around 2001. The spread of GPGPU has been accelerated with introduction of CUDA from NVIDIA in 2006 and later OpenCL in 2009. Nowadays GPGPU is widely deployed in various applications, such as data mining, artificial intelligence, and many scientific computations. GPGPU seemingly promises immense parallelism with massive concurrent cores, and thus much shorter run times. This is true for algorithms that bear intrinsic data and task parallelism, such as image and video processing. For an ATPG system where some algorithms are sequential in nature, the speedup is not easy to achieve in the real world. Flaws in setting up speedup evaluation can lead to false promises. Will GPGPU-based ATPG system become a reality? Or it is just a myth. In this paper, we try to provide an answer by surveying state-of-the-art works and by analyzing practical aspects of today’s industrial designs.