A configurable architecture for running hybrid convolutional neural networks in low-density FPGAs
Convolutional neural networks have become the state of the art of machine learning for a vast set of applications, especially for image classification and object detection. There are several advantages to running inference on these models at the edge, including real-time performance and data privacy...
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| Outros Autores: | , , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2021
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.21/12726 |
| País: | Portugal |
| Oai: | oai:repositorio.ipl.pt:10400.21/12726 |