Neuraghe: exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs

Meloni, Paolo;Deriu, Gianfranco;Raffo, Luigi;
2018

Abstract

Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition. However, their computational load is significant, motivating the development of CNN-specialized accelerators. This work presents NEURAghe, a flexible and efficient hardware/software solution for the acceleration of CNNs on Zynq SoCs. NEURAghe leverages the synergistic usage of Zynq ARM cores and of a powerful and flexible Convolution-Specific Processor deployed on the reconfigurable logic. The Convolution-Specific Processor embeds both a convolution engine and a programmable soft core, releasing the ARM processors from most of the supervision duties and allowing the accelerator to be controlled by software at an ultra-fine granularity. This methodology opens the way for cooperative heterogeneous computing: While the accelerator takes care of the bulk of the CNN workload, the ARM cores can seamlessly execute hard-to-accelerate parts of the computational graph, taking advantage of the NEON vector engines to further speed up computation. Through the companion NeuDNN SW stack, NEURAghe supports end-to-end CNN-based classification with a peak performance of 169GOps/s, and an energy efficiency of 17GOps/W. Thanks to our heterogeneous computing model, our platform improves upon the state-of-the-art, achieving a frame rate of 5.5 frames per second (fps) on the end-to-end execution of VGG-16 and 6.6fps on ResNet-18.
Inglese
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http://dl.acm.org/citation.cfm?id=J1151
https://dl.acm.org/citation.cfm?doid=3299999.3284357
Esperti anonimi
internazionale
scientifica
Convolutional neural networks; FPGAS; HW accelerator; Image classification; Computer Science (all)
no
Meloni, Paolo; Capotondi, Alessandro; Deriu, Gianfranco; Brian, Michele; Conti, Francesco; Rossi, Davide; Raffo, Luigi; Benini, Luca
1.1 Articolo in rivista
info:eu-repo/semantics/article
1 Contributo su Rivista::1.1 Articolo in rivista
262
8
reserved
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