High-Density Liquid-State Machine Circuitry for Time-Series Forecasting

International Journal of Neural Systems
Josep L RosselloVincent Canals

Abstract

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.

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Citations

Jan 4, 2018·International Journal of Neural Systems·Yicun Ouyang, Hujun Yin
May 16, 2018·International Journal of Neural Systems·Tingfang WuFerrante Neri
Oct 7, 2017·International Journal of Neural Systems·Linqiang PanFerrante Neri
Mar 12, 2019·International Journal of Neural Systems·Ruihan HuSheng Chang
Mar 19, 2019·International Journal of Neural Systems·Fabio Galán-PradoJosep L Rosselló
Oct 30, 2021·International Journal of Neural Systems·Yu XueFerrante Neri

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