Spiking Networks for Improved Cognitive Abilities of Edge Computing Devices

A sudden realization came to our minds while preparing this white paper - mobile phones are the first type of devices that received dedicated math accelerators at a pervasive scale. Such things never got wide adoption before: Intel 8087 co-processor[11], Intel Xeon Phi[2, 5] or Google TPU (Tensor Processing Unit)[6] stayed niche devices that few people use and even fewer develop for. But since the last two years, major mobile phone companies include dedicated co-processors[4] necessary for computational photography enhancement or facial recognition, that are suitable for general machine learning. Currently the dominant analytical approach stores data and runs computations in the Cloud[12]. However Cloud based methods poorly fit to a range of important practical applications including augmented reality, real-time data analysis, real-time user interaction, or processing sensitive data that incur high risks for a company if leaked, stolen or intercepted in transfer. The price of deployed analytical methods is increased by the need to have a permanently working internet connection for users, and cloud hardware rent for service providers.