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.
Neuraghe: Exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs / Meloni, P.; Capotondi, A.; Deriu, G.; Brian, M.; Conti, F.; Rossi, D.; Raffo, L.; Benini, L.. - In: ACM TRANSACTIONS ON RECONFIGURABLE TECHNOLOGY AND SYSTEMS. - ISSN 1936-7406. - 11:3(2018), pp. 1-24.
Data di pubblicazione: | 2018 |
Titolo: | Neuraghe: Exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs |
Autore/i: | Meloni, P.; Capotondi, A.; Deriu, G.; Brian, M.; Conti, F.; Rossi, D.; Raffo, L.; Benini, L. |
Autore/i UNIMORE: | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1145/3284357 |
Rivista: | |
Volume: | 11 |
Fascicolo: | 3 |
Pagina iniziale: | 1 |
Pagina finale: | 24 |
Codice identificativo ISI: | WOS:000457149400005 |
Codice identificativo Scopus: | 2-s2.0-85058815973 |
Citazione: | Neuraghe: Exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs / Meloni, P.; Capotondi, A.; Deriu, G.; Brian, M.; Conti, F.; Rossi, D.; Raffo, L.; Benini, L.. - In: ACM TRANSACTIONS ON RECONFIGURABLE TECHNOLOGY AND SYSTEMS. - ISSN 1936-7406. - 11:3(2018), pp. 1-24. |
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