Although GPUs have found a wider market in high performance computing in recent years, an emerging market is exploding in deep learning and computer vision with companies like Baidu as well as many others continuing to push the processing speed and complexity envelope.
While they are not always at the fore of general processing for Google, Microsoft, and other search giants, GPUs are fertile ground for research into new algorithms and approaches to machine learning and deep neural networks.
At the Nvidia GPU Technology Conference earlier in the year there were several examples of how GPUs were being used in neural network training and for near-real time execution of complex machine learning algorithms in natural language processing, image recognition, and rapid video analysis. The compelling angle was that all of these applications were pushing into the ever-increasing need for real-time recognition and output.
Outside of providing web-based services (for instance, automatically tagging images or picking out semantic understanding from video) the real use cases for how GPUs will power real-time services off the web are still developing. Pedestrian detection is one of those areas where, when powered by truly accurate and real-time capabilities, could mean an entirely new wave of potential services around surveillance, traffic systems, driverless cars, and beyond.