Oscillatory Neural Networks Using VO2 Based Phase Encoded Logic

Núñez, Juan; Avedillo, M.J.; Jiménèz, Manuel; Quintana, J. M.; Todri‐Sanial, Aida; Corti, Elisabetta; Karg, Siegfried; Linares-Barranco, B.

doi:10.3389/fnins.2021.655823

Cited by 25 publications

(21 citation statements)

References 24 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The phase transition function has already been observed (Nez et al, 2021 ) but to the best of our knowledge, no closed-form expression has ever been reported. To obtain the transition function, we solve node voltage equations analytically for two coupled oscillators during initialization (see Supplementary Material ).…”

Section: Methodsmentioning

confidence: 82%

Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

Delacour

Todri‐Sanial

2021

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.

show abstract

Section: Methodsmentioning

confidence: 82%

Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

Delacour

Todri‐Sanial

2021

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In ONNs, information is encoded in the phase difference between oscillators, which also reduces signal voltage amplitude and, consequently, reduces power consumption. Currently, ONNs are being explored from various aspects such as materials [14], devices [15][16][17][18], analog circuit design [19,20] to implementation and applications [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Oscillatory Neural Networks for Obstacle Avoidance on Mobile Surveillance Robot E4

Abernot

Gil

Kurylin³

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

Self Cite

View full text Add to dashboard Cite

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

“…Oscillatory Neural Networks (ONNs) are novel neuromorphic architecture enabling low-power computing suitable for edge applications [2][3][4][5][6]. By default, ONN is an analog computing paradigm based on coupled oscillators with phase-encoded information.…”

Section: Introductionmentioning

confidence: 99%

On-Chip Learning with a 15-neuron Digital Oscillatory Neural Network Implemented on ZYNQ Processor

Abernot

Gil

Todri‐Sanial

2022

Proceedings of the International Conference on Neuromorphic Systems 2022

Self Cite

View full text Add to dashboard Cite

Real-time on-chip learning is an important feature for current neuromorphic computing to enable smart embedded systems capable of learning. Neuromorphic computing based on Oscillatory Neural Networks (ONNs) are networks of coupled oscillators computing with phase information. ONNs with fully-connected connections can perform autoassociative memory applications when trained with unsupervised learning rules. In this paper, we propose for the first time an architecture to perform on-chip learning with a digitally implemented ONN. We implement the digital ONN with programmable logic of a ZYNQ processor and we perform learning on the processing system of the same chip. We validate our solution on a 15-neuron ONN trained with either Hebbian or Storkey learning rules up to three patterns. We report a stable resource utilization for both learning rules and timing from 119 µs (Hebbian) to 163 µs (Storkey). Additionally, accuracy is equal to the off-chip learning implementation.

show abstract

Oscillatory Neural Networks Using VO2 Based Phase Encoded Logic

Cited by 25 publications

References 24 publications

Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

Oscillatory Neural Networks for Obstacle Avoidance on Mobile Surveillance Robot E4

On-Chip Learning with a 15-neuron Digital Oscillatory Neural Network Implemented on ZYNQ Processor

Contact Info

Product

Resources

About