FPGA-Based AI System Recognizes Faces at 1,000 Images per Second EE Times

There is tremendous potential for facial recognition technology, such as informing visually impaired persons if someone they know is approaching them. I find it difficult to believe just how fast things are moving with regard to using artificial neural networks (ANNs) and deep learning techniques (for example, see Deep learning machine vision system aids blind and visually impaired, Deep learning hits a sweet note, Machine learning platform speeds optimization of vision systems, Unlocking the power of AI for all developers, and Push-button generation of deep neural networks). Of course, one really interesting application is to perform object detection and identification, including the really tricky task of recognizing and identifying faces in images and videos. This sort of task benefits from the extreme parallelism offered by FPGAs. Of particular interest are Intel's current generation of FPGAs, whose hard-core DSP slices offer both fixed-point and floating-point capabilities, making them suitable for a wide range of artificial intelligence (AI) and embedded vision applications.