Hardware-accelerated spike train generation for neuromorphic image and video processing

Recent studies concerning Spiking Neural Networks show that they are a powerful tool for multiple applications as pattern recognition, image tracking, and detection tasks. The basic functional properties of SNN reside in the use of spike information encoding as the neurons are specifically designed and trained using spike trains. We present a novel and efficient frequency encoding algorithm with Gabor-like receptive fields using probabilistic methods and targeted to FPGA for online pro-cessing. The proposed encoding is versatile, modular and, when applied to images, it is able to perform simple image transforms as edge detection, spot detection or removal, and Gabor-like filtering without any further computation requirements. The algorithm is implemented in FPGA and ready to be used in embedded systems, being capable of processing images or video stream up to 40 megapixel per second per single core. Results show an improvement in hardware occupation and encoding speed up to 2.5x over existing state of the art implementations.