Event-Driven Configurable Module with Refractory Mechanism for ConvNets on FPGA
| Autor: | Camuñas Mesa, Luis Alejandro, Domínguez Cordero, Yaisel L., Serrano Gotarredona, María Teresa, Linares Barranco, Bernabé |
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| Přispěvatelé: | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo, European Union (UE), Ministerio de Ciencia, Innovación y Universidades (MICINN). España, Junta de Andalucía |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | idUS: Depósito de Investigación de la Universidad de Sevilla Universidad de Sevilla (US) idUS. Depósito de Investigación de la Universidad de Sevilla instname |
| Popis: | We have developed a fully configurable event-driven convolutional module with refractory period mechanism that can be used to implement arbitrary Convolutional Neural Networks (ConvNets) on FPGAs following a 2D array structure. Using this module, we have implemented in a Spartan6 FPGA a 4-layer ConvNet with 22 convolutional modules trained for poker card symbol recognition. It has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS) in 1s time. A traffic control mechanism is implemented to downsample high speed input stimuli while keeping spatio-temporal correlation. For slow stimulus play back, a 96% recognition rate is achieved with a power consumption of 0.85mW. At maximum play back speed, the recognition rate is still above 63% when less than 20% of the input events are processed. European Union's Horizon 2020 644096 (ECOMODE European Union's Horizon 2020 No 687299 NeuRAM Ministerio de Ciencia, Innovación y Universidades TEC2015- 63884-C2-1-P Junta de Andalucía TIC-6091 |
| Databáze: | OpenAIRE |
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