Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/1222
Title: | CoNNA - Compressed CNN hardware accelerator | Authors: | Struharik, Rastislav Vukobratovic B. Erdeljan, Andrea Rakanović, Damjan |
Issue Date: | 12-Oct-2018 | Journal: | Proceedings - 21st Euromicro Conference on Digital System Design, DSD 2018 | Abstract: | © 2018 IEEE. In this paper we propose a novel Convolutional Neural Network hardware accelerator, called CoNNA, capable of accelerating pruned, quantized, CNNs. In contrast to most existing solutions, CoNNA offers a complete solution to the full, compressed CNN acceleration, being able to accelerate all layer types commonly found in contemporary CNNs. CoNNA is designed as a coarse-grained reconfigurable architecture, which uses rapid, dynamic reconfiguration during CNN layer processing. Furthermore, by being able to directly process compressed feature and kernel maps, CoNNA is able to achieve higher CNN processing efficiency than some of the previously proposed solutions. Results of the experiments indicate that CoNNA architecture is up to 14.10 times faster than previously proposed MIT's Eyeriss CNN accelerator, up to 6.05 times faster than NullHop CNN accelerator, and up to 4.91 times faster than NVIDIA's Deep Learning Accelerator (NVDLA), while using identical number of computing units and operating at the same clock frequency. | URI: | https://open.uns.ac.rs/handle/123456789/1222 | ISBN: | 9781538673768 | DOI: | 10.1109/DSD.2018.00070 |
Appears in Collections: | FTN Publikacije/Publications |
Show full item record
SCOPUSTM
Citations
12
checked on Apr 29, 2023
Page view(s)
24
Last Week
10
10
Last month
0
0
checked on May 10, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.