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

Authors: Marco Cococcioni; Federico Rossi; Emanuele Ruffaldi; Sergio Saponara;

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

Abstract

With the pervasiveness of deep neural networks in scenarios that bring real-time requirements, there is the increasing need for optimized arithmetic on high performance architectures. In this paper we adopt two key visions: i) extensive use of vectorization to accelerate computation of deep neural network kernels; ii) adoption of the posit compressed arithmetic in order to reduce the memory transfers between the vector registers and the rest of the memory architecture. Finally, we present our first results on a real hardware implementation of the ARM Scalable Vector Extension.

Country
Italy
Related Organizations
Keywords

Posit arithmetic, ARM SVE, Alternative representation of reals, HPC, Vectorization, Alternative representation of reals; ARM SVE; HPC; Posit arithmetic; Vectorization

19 references, page 1 of 2

1. M. Cococcioni, F. Rossi, E. Ru aldi, and S. Saponara, \Fast deep neural networks for image processing using posits and ARM scalable vector extension," Journal of Real-Time Image Processing, pp. 1{13, 2020.

2. ||, \Vectorizing posit operations on RISC-V for faster deep neural networks: experiments and comparison with ARM SVE," Journal of Neural Computing and Applications, vol. 33, pp. 10 575{{10 585, 2021. [Online]. Available: https://doi.org/10.1007/s00521-021-05814-0

3. M. Cococcioni, F. Rossi, E. Ru aldi, and S. Saponara, \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, N. Kehtarnavaz and M. F. Carlsohn, Eds., vol. 11736, International Society for Optics and Photonics. SPIE, 2021, pp. 19 { 25. [Online]. Available: https://doi.org/10.1117/12.2586565

4. N. Burgess, J. Milanovic, N. Stephens, K. Monachopoulos, and D. Mansell, \B oat16 processing for neural networks," in 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH), June 2019, pp. 88{91.

5. U. Koster, T. Webb, X. Wang, M. Nassar, A. K. Bansal, W. Constable, O. Elibol, S. Gray, S. Hall, L. Hornof et al., \Flexpoint: An adaptive numerical format for e cient training of deep neural networks," in In Proc. of teh 31st Conference on Neural Information Processing Systems (NIPS'17), 2017, pp. 1742{1752.

6. V. Popescu, M. Nassar, X. Wang, E. Tumer, and T. Webb, \Flexpoint: Predictive numerics for deep learning," in In Proc. of the 25th IEEE Symposium on Computer Arithmetic (ARITH'18), June 2018, pp. 1{4.

7. N. Mellempudi, S. Srinivasan, D. Das, and B. Kaul, \Mixed precision training with 8-bit oating point," 05 2019.

8. J. L. Gustafson, The End of Error: Unum Computing. Chapman and Hall/CRC, 2015.

9. ||, \A radical approach to computation with real numbers," Supercomputing Frontiers and Innovations, vol. 3, no. 2, pp. 38{53, 2016.

10. J. L. Gustafson and I. T. Yonemoto, \Beating oating point at its own game: Posit arithmetic," Supercomputing Frontiers and Innovations, vol. 4, no. 2, pp. 71{86, 2017. [OpenAIRE]

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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EC| EPI SGA1
Project
EPI SGA1
SGA1 (Specific Grant Agreement 1) OF THE EUROPEAN PROCESSOR INITIATIVE (EPI)
  • Funder: European Commission (EC)
  • Project Code: 826647
  • Funding stream: H2020 | SGA-RIA
,
EC| TEXTAROSSA
Project
TEXTAROSSA
Towards EXtreme scale Technologies and Accelerators for euROhpc hw/Sw Supercomputing Applications for exascale
  • Funder: European Commission (EC)
  • Project Code: 956831
  • Funding stream: H2020 | EuroHPC-RIA
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