Self-Organizing Maps Hybrid Implementation Based on Stochastic Computing

Internet of Things (IoT) applications and mobile systems are more and more dependent on Machine Learning based solutions, thus requiring a big computational power with a low cost in terms of power consumption. This fact has revived the interest in nonconventional hardware computing methods capable to implement complex functions in a simple way in contrast with the conventional ones. This work proposes a novel hardware/software hybrid Self-Organizing Map (SOM) implementation using stochastic computing. In turn, to support this development, several stochastic block designs are presented as the squared Euclidian distance, and the Winner-Take-All (WTA) similarity check. The capabilities and performance of the methodology is tested over a well-known classification task as the Iris flower benchmark, archiving the same classification performance than the software solutions. The proposed solution presents a low-cost methodology in terms of hardware resources and power, due to its inherent capacity to implement complex functions in a simple way. This enables the methodology to implement large self-learning classifiers based on SOM with low hardware requirements.