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Neural Computing and Applications

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27-08-2016 | Original Article

Artificial neural networks based dynamic priority arbitration for asynchronous flow control

Authors: Syed Rameez Naqvi, Tallha Akram, Sajjad Ali Haider, Muhammad Kamran

Published in: Neural Computing and Applications | Issue 7/2018

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Abstract

Accesses to physical links in Networks-on-Chip need to be appropriately arbitrated to avoid collisions. In the case of asynchronous routers, this arbitration between various clients, carrying messages with different service levels, is managed by dedicated circuits called arbiters. The latter are accustomed to allocate the shared resource to each client in a round-robin fashion; however, they may be tuned to favor certain messages more frequently by means of various digital design techniques. In this work, we make use of artificial neural networks to propose a mechanism to dynamically compute priority for each message by defining a few constraints. Based on these constraints, we first build a mathematical model for the objective function, and propose two algorithms for vector selection and resource allocation to train the artificial neural networks. We carry out a detailed comparison between seven different learning algorithms, and observe their effectiveness in terms of prediction efficiency for the application of dynamic priority arbitration. The decision is based on input parameters: available tokens, service levels, and an active request from each client. The performance of the learning algorithms has been analyzed in terms of mean squared error, true acceptance rate, number of epochs and execution time, so as to ensure mutual exclusion.

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Our particular application does not need run-time training, since our architecture, the number of inputs and outputs, number of virtual channels (i.e., clients) usually will all remain fixed during the operation—so offline training once will be sufficient.

Chapiro DM (1984) Globally-asynchronous locally-synchronous systems. Ph.D. thesis, Stanford University

Agarwal A, Iskander C, Shankar R (2009) Survey of NoC architectures and contributions. Eng Comput Archit 3(1)

Beigne E, Clermidy F, Vivet P, Clouard A, Renaudin M (2005) Proceedings of 11th IEEE symposium on asynchronous circuits and systems (ASYNC 2005), pp 54–63. doi:10.1109/ASYNC.2005.10

Beigne E, Vivet P (2006) Proceedings of the 12th IEEE international symposium on asynchronous circuits and systems. IEEE Computer Society, Washington, DC, USA, 2006. ASYNC ’06, p 172. doi:10.1109/ASYNC.2006.16

Dally W, Towles B (2001) Design automation conference, 2001. Proceedings (2001), pp 684–689. doi:10.1109/DAC.2001.156225

Naqvi S, Najvirt R, Steininger A (2013) 2013 IEEE 16th International Symposium on design and diagnostics of electronic circuits systems (DDECS), pp 153–158. doi:10.1109/DDECS.2013.6549808

Najvirt R, Naqvi S, Steininger A (2013) 2013 IEEE 19th international symposium on asynchronous circuits and systems (ASYNC), pp 115–123. doi:10.1109/ASYNC.2013.25

Bjerregaard T (2005) Sparso, in design, automation and test in Europe, 2005. Proceedings, vol 2, pp 1226–1231.doi:10.1109/DATE.2005.36

Rostislav DR, Vishnyakov V, Friedman E, Ginosar R (2005) Proceedings of the 11th IEEE international symposium on asynchronous circuits and systems. IEEE Computer Society, Washington, DC, USA, 2005. ASYNC ’05, pp 44–53. doi:10.1109/ASYNC.2005.11

10.

Dobkin RR, Ginosar R, Kolodny A (2009) QNoC asynchronous router. Integr VLSI J 42(2):103. doi:10.1016/j.vlsi.2008.03.001

11.

Dobkin R, Ginosar R, Cidon I (2007) First international symposium on Networks-on-Chip, 2007. NOCS 2007, p 218. doi:10.1109/NOCS.2007.36

12.

Feliciian F, Furber S (2004) IEEE international SOC conference, 2004 Proceedings, pp 274–277. doi:10.1109/SOCC.2004.1362432

13.

Naqvi SR (2012) ICCGI 2012

14.

Sparso J, Furber S (2010) Principles of asynchronous circuit design: a systems perspective, 1st edn. Springer, Berlin

15.

Ogras UY, Marculescu R (2013) Modeling, analysis and optimization of network-on-chip communication architectures. Springer, BerlinCrossRef

16.

Naqvi SR, Steininger A (2014) Proceedings of the conference on design, automation & test in Europe. European Design and Automation Association, 3001 Leuven, Belgium, Belgium, 2014, DATE ’14, pp 295:1–295:6. http://dl.acm.org/citation.cfm?id=2616606.2617035

17.

Kinniment DJ (2007) Synchronization and arbitration in digital systems. Wiley, New YorkCrossRef

18.

Dally W, Towles B (2003) Principles and practices of interconnection networks. Morgan Kaufmann Publishers Inc., San Francisco

19.

Duato J, Yalamanchili S, Lionel N (2002) Interconnection networks: an engineering approach. Morgan Kaufmann Publishers Inc., San Francisco

20.

Bjerregaard T, Sparso J (2005) Proceedings of 11th IEEE international symposium on asynchronous circuits and systems, 2005. ASYNC 2005, pp 34 – 43. doi:10.1109/ASYNC.2005.7

21.

Felicijan T, Bainbridge J, Furber S (2003) Proceedings of the 15th international conference on microelectronics, 2003. ICM 2003, pp 123–126. doi:10.1109/ICM.2003.1287737

22.

Dimitrakopoulos G, Chrysos N, Galanopoulos K (2008) IEEE international conference on computer design, ICCD 2008, pp 664–670. doi:10.1109/ICCD.2008.4751932

23.

Foo S, Saratchandran P, Sundararajan N (1993) Proceedings of 1993 international joint conference on neural networks, 1993. IJCNN ’93-Nagoya, vol 3, pp 3058–3061. doi:10.1109/IJCNN.1993.714365

24.

Onuki J, Maenosono T, Shibata M, Iijima N, Mitsui H, Yoshida Y, Sone M (1993) Proceedings of 1993 international joint conference on neural networks, 1993. IJCNN ’93-Nagoya, vol 2, pp 1913–1916. doi:10.1109/IJCNN.1993.717029

25.

Speckmann H, Thole P, Rosenstiel W (1993) Proceedings of 1993 international joint conference on neural networks, 1993. IJCNN ’93-Nagoya, vol 2, pp 1951–1954. doi:10.1109/IJCNN.1993.717038

26.

Muller DE, Bartky WS (1959) Proceedings of international symposium on theory of switching, part 1. Harvard University Press, MassachusettsMATH

27.

Yakovlev A, Petrov A, Lavagno L (1994) Very large scale integration (VLSI) systems. IEEE Trans 2(3):372. doi:10.1109/92.311648

28.

Ghiribaldi A, Bertozzi D, Nowick SM (2013) Design automation test in europe conference exhibition (DATE), pp 332–337. doi:10.7873/DATE.2013.079

29.

G. Miorandi, D. Bertozzi, S. Nowick (2015) 2015 21st IEEE international symposium on asynchronous circuits and systems (ASYNC), pp 108–115. doi:10.1109/ASYNC.2015.24

30.

Lent B (1982) A variable priority arbiter for resource allocation in asynchronous multiprocessor systems. Microprocess Microprogr 9(5):299. doi:10.1016/0165-6074(82)90010-2

31.

Hasasneh N, Bell I, Jesshope C (2007) Architectural premises for pervasive computing (ARCS 06). J Syst Arch 53(56):253. doi:10.1016/j.sysarc.2006.10.004

32.

Chouvardas VG, Antoniades I, Hatalis M, Bleris GL (2008) Resource arbitration using neural networks. citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.77.5302&rep=rep1&type=pdf

33.

Meireles M, Almeida P, Simoes M (2003) A comprehensive review for industrial applications of artificial neural networks. Ind Electron IEEE Trans 50(3):585. doi:10.1109/TIE.2003.812470

34.

Azar AT (2013) Fast neural network learning algorithms for medical applications. Neural Comput Appl 23(3–4):1019CrossRef

35.

Guojin C, Miaofen Z, Honghao Y, Yan L (2007) IEEE international conference on signal processing and communications, ICSPC 2007, pp 1207–1210. doi:10.1109/ICSPC.2007.4728542

36.

Güçlü U, van Gerven MAJ (2015) Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J Neurosci 35(27):10005. doi:10.1523/jneurosci.5023-14.2015

37.

Bielecki A (2003) Mathematical model of architecture and learning processes of neural networks. TASK quarterly : scientific bulletin of academic computer centre in Gdansk 7(1):93

38.

Nouir Z, Sayrac B, Fourestié B, Tabbara W, Brouaye F (2007) 13th European wireless conference, Paris, France

39.

Reed R (1993) Pruning algorithms-a survey. Trans Neural Netw 4(5):740. doi:10.1109/72.248452

40.

Marquardt DW (1963) An alogorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431CrossRefMATH

41.

Beale E (1972) A derivation of conjugate gradients. Numerical methods for nonlinear optimization, pp 39–43

42.

Gill PE, Murray W, Wright MH (1981) Practical Optimization. Academic Press

43.

Hestenes MR (2012) Conjugate direction methods in optimization, vol 12. Springer, BerlinMATH

44.

Johansson EM, Dowla FU, Goodman DM (1991) Backpropagation learning for multilayer feed-forward neural networks using the conjugate gradient method. Int J Neural Syst 2(04):291CrossRef

45.

Battiti R, Masulli F (1990) International neural network conference. Springer, Berlin

46.

Battiti R (1992) First- and second-order methods for learning: between steepest descent and newton's method. Neural Comput 4(2):141. doi:10.1162/neco.1992.4.2.141

47.

Beale MH, Hagan MT, Demuth HB (2010) Neural network toolbox. Users Guide, MathWorks

48.

Kamran M, Haider SA, Akram T, Naqvi SR, He SK (2016) Prediction of IV curves for a superconductingthin film using artificial neural networks. Superlattices and Microstruct 95:88–94CrossRef

Title: Artificial neural networks based dynamic priority arbitration for asynchronous flow control
Authors: Syed Rameez Naqvi
Tallha Akram
Sajjad Ali Haider
Muhammad Kamran
Publication date: 27-08-2016
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 7/2018
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2571-6

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