@inproceedings{cd61297b5aa74c30bdf08c2886ab49a0,
title = "Spin torque nano-oscillator based Oscillatory Neural Network",
abstract = "Oscillatory Neural Networks (ONN) are becoming a popular neuromorphic computing model owing to their efficient parallel processing capabilities. Hoppensteadt and Izhikevich proposed an ONN architecture resembling associative memory, with Phase-Locked Loop (PLL) circuits as neurons. Unfortunately, there are shortcomings in realizing such architectures due to the inefficiencies of CMOS based implementations of oscillators and other hardware. We propose a PLL structure for ONN applications fashioned using energy efficient and scalable Spin Torque Oscillators (STOs). We demonstrate the functionality of a 60 neuron ONN using STOs for binary image identification.",
keywords = "Associative memory, Frequency locking, LLG, Oscillatory neural network, Phase locked loop, Phase locking, Spin torque oscillators",
author = "Liyanagedera, {Chamika M.} and Karthik Yogendra and Kaushik Roy and Deliang Fan",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Joint Conference on Neural Networks, IJCNN 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = oct,
day = "31",
doi = "10.1109/IJCNN.2016.7727360",
language = "English (US)",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1387--1394",
booktitle = "2016 International Joint Conference on Neural Networks, IJCNN 2016",
}