The edge analytics platform provides the software tools to execute real-time data-analytics methods in the edge. The CUDA model and cuDNN library have been employed for the object detection and tracking applications. The CUDA parallel programming model is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the development and execution of compute kernels.
Within CLASS, CUDA has been exploited to obtain the desired compute capability at the edge. On top of these components, CLASS employs the NVIDIA TensorRT platform that includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. The cross-platform OpenCV library supporting a CUDA-based GPU interface is also used for the implementation of the object detection analytics methods.