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@ARTICLE{Siegel:1006790,
      author       = {Siegel, Sebastian and Bouhadjar, Younes and Tetzlaff, Tom
                      and Waser, R. and Dittmann, Regina and Wouters, Dirk},
      title        = {{S}ystem model of neuromorphic sequence learning on a
                      memristive crossbar array},
      journal      = {Neuromorphic computing and engineering},
      volume       = {3},
      issn         = {2634-4386},
      address      = {Bristol},
      publisher    = {IOP Publishing Ltd.},
      reportid     = {FZJ-2023-01843},
      pages        = {024002},
      year         = {2023},
      abstract     = {Machine learning models for sequence learning and
                      processing often suffer from high energy consumption and
                      require large amounts of training data. The brain presents
                      more efficient solutions to how these types of tasks can be
                      solved. While this has inspired the conception of novel
                      brain-inspired algorithms, their realizations remain
                      constrained to conventional von-Neumann machines. Therefore,
                      the potential power efficiency of the algorithm cannot be
                      exploited due to the inherent memory bottleneck of the
                      computing architecture. Therefore, we present in this paper
                      a dedicated hardware implementation of a biologically
                      plausible version of the Temporal Memory component of the
                      Hierarchical Temporal Memory concept. Our implementation is
                      built on a memristive crossbar array and is the result of a
                      hardware-algorithm co-design process. Rather than using the
                      memristive devices solely for data storage, our approach
                      leverages their specific switching dynamics to propose a
                      formulation of the peripheral circuitry, resulting in a more
                      efficient design. By combining a brain-like algorithm with
                      emerging non-volatile memristive device technology we strive
                      for maximum energy efficiency. We present simulation results
                      on the training of complex high-order sequences and discuss
                      how the system is able to predict in a context-dependent
                      manner. Finally, we investigate the energy consumption
                      during the training and conclude with a discussion of
                      scaling prospects.},
      cin          = {PGI-7 / PGI-15 / PGI-10 / JARA-FIT / INM-6},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)PGI-7-20110106 / I:(DE-Juel1)PGI-15-20210701 /
                      I:(DE-Juel1)PGI-10-20170113 / $I:(DE-82)080009_20140620$ /
                      I:(DE-Juel1)INM-6-20090406},
      pnm          = {5233 - Memristive Materials and Devices (POF4-523) / BMBF
                      16ME0399 - Verbundprojekt: Neuro-inspirierte Technologien
                      der künstlichen Intelligenz für die Elektronik der Zukunft
                      - NEUROTEC II - (BMBF-16ME0399) / BMBF 16ME0398K -
                      Verbundprojekt: Neuro-inspirierte Technologien der
                      künstlichen Intelligenz für die Elektronik der Zukunft -
                      NEUROTEC II - (BMBF-16ME0398K) / ACA - Advanced Computing
                      Architectures (SO-092) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539) / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF4-5233 / G:(DE-82)BMBF-16ME0399 /
                      G:(DE-82)BMBF-16ME0398K / G:(DE-HGF)SO-092 /
                      G:(EU-Grant)945539 / G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001064075800001},
      doi          = {10.1088/2634-4386/acca45},
      url          = {https://juser.fz-juelich.de/record/1006790},
}