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A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation

Opened Access A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation

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Autor: Camuñas Mesa, Luis Alejandro
Domínguez Cordero, Yaisel L.
Linares Barranco, Alejandro
Serrano Gotarredona, María Teresa
Linares Barranco, Bernabé
Departamento: Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores
Fecha: 2018
Publicado en: Frontiers in Neuroscience, 12, 63-.
Tipo de documento: Artículo
Resumen: Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be impleme...
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Cita: Camuñas Mesa, L.A., Domínguez Cordero, Y.L., Linares Barranco, A., Serrano Gotarredona, M.T. y Linares Barranco, B. (2018). A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation. Frontiers in Neuroscience, 12, 63-.
Tamaño: 4.296Mb
Formato: PDF

URI: https://hdl.handle.net/11441/72465

DOI: 10.3389/fnins.2018.00063

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