A Configurable Architecture for Running Hybrid Convolutional Neural Networks in Low-Density FPGAs

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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Bibliographic Details
Main Authors: Mario P. Vestias, Rui P. Duarte, Jose T. De Sousa, Horacio C. Neto
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Convolutional neural network
deep learning
embedded computing
field-programmable gate array
hybrid quantization
Online Access:https://ieeexplore.ieee.org/document/9110581/

Internet

https://ieeexplore.ieee.org/document/9110581/

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