Speed Sign Detection demo uses the Convolutional Neural Network (CNN) for automotive applications. CNNs enables the traditional tasks typically performed by humans to improve a more efficient and faster implementation of data. Parallelization in FPGAs makes them most suited for implementing neural networks.
Convolutional Neural Network used in this demonstration can be trained by passing multiple traffic signs through the untrained model to calculate weights and activations. This creates a trained model of weights and activations read by the CNN Accelerator implemented in ECP5 FPGA. The end result is that the camera can detect and display the speed limits when the sign passes in front of it, displaying the indicated speed.
Low-power, production-priced ECP5 brings best in class power vs. performance efficiency in neural networks implemented. Such Edge implementation keeps processing local thus improving security.