Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capability is highly requested in the embedded system domain for video processing applications such as video surveillance and homeland security. Moreover, with the increasing requirement of portable and ubiquitous processing, power consumption is a key issue to be accounted for.In this paper, we present an FPGA implementation of CNN designed for addressing portability and power efficiency. Performance characterization results show that the proposed implementation is as efficient as a general purpose 16-core CPU, and almost 15 times faster than a SoC GPU for mobile application. Moreover, external memory footprint is reduced by 84% with respect to a standard CNN software application.
BETTONI, M., URGESE, G., Kobayashi, Y., MACII, E., ACQUAVIVA, A. (2017). A Convolutional Neural Network Fully Implemented on FPGA for Embedded Platforms. IEEE [10.1109/NGCAS.2017.16].
A Convolutional Neural Network Fully Implemented on FPGA for Embedded Platforms
ACQUAVIVA, ANDREA
2017
Abstract
Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capability is highly requested in the embedded system domain for video processing applications such as video surveillance and homeland security. Moreover, with the increasing requirement of portable and ubiquitous processing, power consumption is a key issue to be accounted for.In this paper, we present an FPGA implementation of CNN designed for addressing portability and power efficiency. Performance characterization results show that the proposed implementation is as efficient as a general purpose 16-core CPU, and almost 15 times faster than a SoC GPU for mobile application. Moreover, external memory footprint is reduced by 84% with respect to a standard CNN software application.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
