Experimental Results of Vectorized Posit-Based DNNs on a Real ARM SVE High Performance Computing Machine-Reference-Cited by-同舟云学术

Experimental Results of Vectorized Posit-Based DNNs on a Real ARM SVE High Performance Computing Machine

Published:2022 Issue: Volume: Page:61-68
ISSN:1876-1100
Container-title:Lecture Notes in Electrical Engineering
language:
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Author:

Cococcioni Marco^ORCID,Rossi Federico^ORCID,Ruffaldi Emanuele^ORCID,Saponara Sergio^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-95498-7_9

Reference19 articles.

1. Cococcioni, M., Rossi, F., Ruffaldi, E., Saponara, S.: Fast deep neural networks for image processing using posits and ARM scalable vector extension. J. Real-Time Image Process. 17(3), 759–771 (2020). https://doi.org/10.1007/s11554-020-00984-x

2. Cococcioni, M., Rossi, F., Ruffaldi, E., Saponara, S.: Vectorizing posit operations on RISC-V for faster deep neural networks: experiments and comparison with ARM SVE. J. Neural Comput. Appl. 33, 575–585 (2021). https://doi.org/10.1007/s00521-021-05814-0

3. Cococcioni, M., Rossi, F., Ruffaldi, E., Saponara, S.: Faster deep neural network image processing by using vectorized posit operations on a RISC-V processor, In: Real-Time Image Processing and Deep Learning 2021, Kehtarnavaz, N., Carlsohn, M.F. (Eds.,) International Society for Optics and Photonics. SPIE, vol. 11736, pp. 19–25 (2021). https://doi.org/10.1117/12.2586565

4. Burgess, N., Milanovic, J., Stephens, N., Monachopoulos, K., Mansell, D.: Bfloat16 processing for neural networks. In: 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), pp. 88–91 (2019)

5. Koster, U., et al.: Flexpoint: an adaptive numerical format for efficient training of deep neural networks. In: Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017) (2017)