Implementation of 14 bits floating point numbers of calculating units for neural network hardware development; IOP Conference Series: Materials Science and Engineering; Vol. 177 : Mechanical Engineering, Automation and Control Systems (MEACS 2016)
| Parent link: | IOP Conference Series: Materials Science and Engineering Vol. 177 : Mechanical Engineering, Automation and Control Systems (MEACS 2016).— 2017.— [012044, 5 p.] |
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| Collectivité auteur: | |
| Autres auteurs: | , , , |
| Résumé: | Title screen An important aspect of modern automation is machine learning. Specifically, neural networks are used for environment analysis and decision making based on available data. This article covers the most frequently performed operations on floating-point numbers in artificial neural networks. Also, a selection of the optimum value of the bit to 14-bit floating-point numbers for implementation on FPGAs was submitted based on the modern architecture of integrated circuits. The description of the floating-point multiplication (multiplier) algorithm was presented. In addition, features of the addition (adder) and subtraction (subtractor) operations were described in the article. Furthermore, operations for such variety of neural networks as a convolution network - mathematical comparison of a floating point ('less than' and 'greater than or equal') were presented. In conclusion, the comparison with calculating units of Atlera was made. |
| Langue: | anglais |
| Publié: |
2017
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| Collection: | Information technologies in Mechanical Engineering |
| Sujets: | |
| Accès en ligne: | http://dx.doi.org/10.1088/1757-899X/177/1/012044 http://earchive.tpu.ru/handle/11683/37851 |
| Format: | Électronique Chapitre de livre |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=654045 |
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| 200 | 1 | |a Implementation of 14 bits floating point numbers of calculating units for neural network hardware development |f I. V. Zoev [et al.] | |
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| 225 | 1 | |a Information technologies in Mechanical Engineering | |
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| 330 | |a An important aspect of modern automation is machine learning. Specifically, neural networks are used for environment analysis and decision making based on available data. This article covers the most frequently performed operations on floating-point numbers in artificial neural networks. Also, a selection of the optimum value of the bit to 14-bit floating-point numbers for implementation on FPGAs was submitted based on the modern architecture of integrated circuits. The description of the floating-point multiplication (multiplier) algorithm was presented. In addition, features of the addition (adder) and subtraction (subtractor) operations were described in the article. Furthermore, operations for such variety of neural networks as a convolution network - mathematical comparison of a floating point ('less than' and 'greater than or equal') were presented. In conclusion, the comparison with calculating units of Atlera was made. | ||
| 461 | 0 | |0 (RuTPU)RU\TPU\network\2008 |t IOP Conference Series: Materials Science and Engineering | |
| 463 | 0 | |0 (RuTPU)RU\TPU\network\19514 |t Vol. 177 : Mechanical Engineering, Automation and Control Systems (MEACS 2016) |o International Conference, October 27–29, 2016, Tomsk, Russia |o [proceedings] |f National Research Tomsk Polytechnic University (TPU) ; eds. A. P. Zykova ; N. V. Martyushev |v [012044, 5 p.] |d 2017 | |
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