Ponencia
Conversion of Synchronous Artificial Neural Network to Asynchronous Spiking Neural Network using sigma-delta quantization
Autor/es | Yousefzadeh, Amirreza
Hosseini, Sahar Holanda, Priscila Leroux, Sam Werner, Thilo Serrano Gotarredona, María Teresa Linares Barranco, Bernabé Dhoedt, Bart Simoens, Pieter |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2019 |
Fecha de depósito | 2020-10-19 |
Publicado en |
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ISBN/ISSN | 978-1-5386-7884-8 |
Resumen | Artificial Neural Networks (ANNs) show great performance
in several data analysis tasks including visual and auditory
applications. However, direct implementation of these algorithms without
considering the sparsity of ... Artificial Neural Networks (ANNs) show great performance in several data analysis tasks including visual and auditory applications. However, direct implementation of these algorithms without considering the sparsity of data requires high processing power, consume vast amounts of energy and suffer from scalability issues. Inspired by biology, one of the methods which can reduce power consumption and allow scalability in the implementation of neural networks is asynchronous processing and communication by means of action potentials, so-called spikes. In this work, we use the wellknown sigma-delta quantization method and introduce an easy and straightforward solution to convert an Artificial Neural Network to a Spiking Neural Network which can be implemented asynchronously in a neuromorphic platform. Briefly, we used asynchronous spikes to communicate the quantized output activations of the neurons. Despite the fact that our proposed mechanism is simple and applicable to a wide range of different ANNs, it outperforms the state-of-the-art implementations from the accuracy and energy consumption point of view. All source code for this project is available upon request for the academic purpose |
Agencias financiadoras | European Union (UE) Ministerio de Economía y Competitividad (MINECO). España |
Identificador del proyecto | Horizon 2020 No 687299 NeuRAM
TEC2015-63884-C2-1-P |
Cita | Yousefzadeh, A., Hosseini, S., Holanda, P., Leroux, S., Werner, T., Serrano Gotarredona, M.T.,...,Simoens, P. (2019). Conversion of Synchronous Artificial Neural Network to Asynchronous Spiking Neural Network using sigma-delta quantization. En AICAS 2019: IEEE International Conference on Artificial Intelligence Circuits and Systems (81-85), Hsinchu, Taiwan: IEEE Computer Society. |
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