Brain Floating Point Precision for Object Detector

As the size of the deep neural network (DNN) model increases, the memory usage and computational time also increase. Deeper neural network models require huge memory size and high computational power. For edge devices, it is not feasible to run larger models with full precision. We present the usage of half precision data format which accelerates the computational time of DNN models and reduces the size of the final hardware while losing little accuracy.