Self-Organizing Maps hybrid Implementation Based on Stochastic Computing
| Autor: | Eugeni Isern, Alejandro Morán, Josep L. Rosselló, Miquel Roca, Víctor Martínez-Moll, Vincent Canals |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Self-organizing map
Stochastic computing Computer science business.industry Probabilistic logic 02 engineering and technology Software Computer engineering 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Unsupervised learning 020201 artificial intelligence & image processing business Field-programmable gate array Block (data storage) |
| Zdroj: | DCIS |
| DOI: | 10.1109/dcis201949030.2019.8959841 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|