VVAS enables application developers to construct seamless streaming pipelines for AI-based video and image analytics and various other solutions without requiring any comprehension of FPGA or other complex development environments. VVAS comes equipped with a multitude of hardware accelerators for various functionalities, along with highly optimized GStreamer plugins, so you can execute any Neural Network model with a full suite of infrastructure support for an end-to-end use case implementation. This makes it an excellent choice for video analytics tasks. The design is optimized for machine learning (ML) operations on the Neural Processing Unit (NPU) and leverages hardware-accelerated AMD Vitis™ kernels to efficiently handle preprocessing, postprocessing, and inference.
For advanced developers, VVAS offers a user-friendly framework that allows for integration of their own hardware accelerators/kernels into GStreamer-based applications. VVAS also provides support for popular classification models such as resnet50 and resnet18.
All of this infrastructure grants the flexibility for swift prototyping and progression to full-fledged production-level solutions, thereby significantly reducing the time-to-market for AMD platform-based solutions.