Real world deep learning applications involve multi-stage data processing pipelines which include many compute intensive preprocessing operations like data loading from disk, decoding, resizing, color space conversion, scaling, and cropping multiple ML networks of different kinds like CNN, and various post-processing operations like NMS.
The AI kernel scheduler (AKS) is an application to automatically and efficiently pipeline such graphs without much effort from the users. It provides various kinds of kernels for every stage of the complex graphs which are plug and play and are highly configurable. For example, preprocessing kernels like image decode and resize, CNN kernel like the Vitis AI DPU kernel and post processing kernels like SoftMax and NMS. You can create their graphs using kernels and execute their jobs seamlessly to get the maximum performance.
For more details and examples, see the Vitis AI GitHub (AI Kernel Scheduler).