skip to main content
10.1145/2834892.2834895acmconferencesArticle/Chapter ViewAbstractPublication PagesscConference Proceedingsconference-collections
short-paper

Dynamic adaptive neural network arrays: a neuromorphic architecture

Published:15 November 2015Publication History

ABSTRACT

Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

References

  1. B. V. Benjamin, P. Gao, E. McQuinn, S. Choudhary, A. R. Chandrasekaran, J.-M. Bussat, R. Alvarez-Icaza, J. V. Arthur, P. Merolla, K. Boahen, et al. Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations. Proceedings of the IEEE, 102(5):699--716, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  2. M. E. Dean, C. D. Schuman, and J. D. Birdwell. Dynamic adaptive neural network array. In Unconventional Computation and Natural Computation, pages 129--141. Springer, 2014.Google ScholarGoogle Scholar
  3. M. Drouhard, C. D. Schuman, J. D. Birdwell, and M. E. Dean. Visual analytics for neuroscience-inspired dynamic architectures. In Foundations of Computational Intelligence (FOCI), 2014 IEEE Symposium on, pages 106--113. IEEE, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  4. S. B. Furber, D. R. Lester, L. Plana, J. D. Garside, E. Painkras, S. Temple, A. D. Brown, et al. Overview of the spinnaker system architecture. Computers, IEEE Transactions on, 62(12):2454--2467, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. G. Indiveri. Neuromorphic engineering. In Springer Handbook of Computational Intelligence, pages 715--725. Springer, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  6. G. Indiveri, R. Legenstein, G. Deligeorgis, T. Prodromakis, et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures. Nanotechnology, 24(38):384010, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. H. Jo, T. Chang, I. Ebong, B. B. Bhadviya, P. Mazumder, and W. Lu. Nanoscale memristor device as synapse in neuromorphic systems. Nano letters, 10(4):1297--1301, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  8. M. Lichman. UCI machine learning repository, 2013.Google ScholarGoogle Scholar
  9. P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 345(6197):668--673, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  10. T. Pfeil, A. Grübl, S. Jeltsch, E. Müller, P. Müller, M. A. Petrovici, M. Schmuker, D. Brüderle, J. Schemmel, and K. Meier. Six networks on a universal neuromorphic computing substrate. Frontiers in neuroscience, 7, 2013.Google ScholarGoogle Scholar
  11. J. Schemmel, D. Bruderle, A. Grubl, M. Hock, K. Meier, and S. Millner. A wafer-scale neuromorphic hardware system for large-scale neural modeling. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pages 1947--1950. IEEE, 2010.Google ScholarGoogle Scholar
  12. C. D. Schuman and J. D. Birdwell. Dynamic artificial neural networks with affective systems. PloS one, 8(11):e80455, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  13. C. D. Schuman and J. D. Birdwell. Variable structure dynamic artificial neural networks. Biologically Inspired Cognitive Architectures, 6:126--130, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  14. C. D. Schuman, J. D. Birdwell, and M. Dean. Neuroscience-inspired dynamic architectures. In Biomedical Science and Engineering Center Conference (BSEC), 2014 Annual Oak Ridge National Laboratory, pages 1--4. IEEE, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  15. C. D. Schuman, J. D. Birdwell, and M. E. Dean. Spatiotemporal classification using neuroscience-inspired dynamic architectures. Procedia Computer Science, 41:89--97, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  16. M. Soltiz, D. Kudithipudi, C. Merkel, G. S. Rose, and R. E. Pino. Memristor-based neural logic blocks for nonlinearly separable functions. Computers, IEEE Transactions on, 62(8):1597--1606, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Dynamic adaptive neural network arrays: a neuromorphic architecture

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            MLHPC '15: Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments
            November 2015
            40 pages
            ISBN:9781450340069
            DOI:10.1145/2834892

            Copyright © 2015 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 15 November 2015

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • short-paper

            Acceptance Rates

            MLHPC '15 Paper Acceptance Rate5of7submissions,71%Overall Acceptance Rate5of7submissions,71%

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader