Embedded, GPU-accelerated computing is the use of a graphics processing unit (GPU) combined with a central processing unit (CPU) to facilitate advanced computing processes such as Artificial Intelligence of Things (AIoT), Deep Learning, Prototyping/Pre-concept design, CUDA development and medical Hi-Res imaging, to name a few.

Embedded, GPU-accelerated computing is gaining popularity as its potential becomes realized in many high-tech labs and R&D facilities around the world. GPU-accelerated computing plays a huge role in accelerating applications in platforms within these bleeding-edge industries. Whether it is designing self-driving cars, rendering ultra-HD scenes with millions of polygons (4K and above), or working with advanced CAD models for GIS mapping, a multi-GPU system will significantly increase the performance and capabilities advanced developers have available.
Through embedded GPU-accelerated technology, processing and computing times can be cut into a fraction of the time of what could be accomplished compared to traditional, less-sophisticated computing infrastructures. The combination of high-performance computing along with lower power consumption can result in a more significant and more extensive deep learning in neural networks.