  Neural networks have established as a generic and powerful means to approach
challenging problems such as image classification, object detection or decision
making. Their successful employment foots on an enormous demand of compute. The
quantization of network parameters and the processed data has proven a valuable
measure to reduce the challenges of network inference so effectively that the
feasible scope of applications is expanded even into the embedded domain. This
paper describes the making of a real-time object detection in a live video
stream processed on an embedded all-programmable device. The presented case
illustrates how the required processing is tamed and parallelized across both
the CPU cores and the programmable logic and how the most suitable resources
and powerful extensions, such as NEON vectorization, are leveraged for the
individual processing steps. The crafted result is an extended Darknet
framework implementing a fully integrated, end-to-end solution from video
capture over object annotation to video output applying neural network
inference at different quantization levels running at 16~frames per second on
an embedded Zynq UltraScale+ (XCZU3EG) platform.
