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@INPROCEEDINGS{Siegel:1037670,
      author       = {Siegel, Sebastian and Ziegler, Tobias and Bouhadjar, Younes
                      and Tetzlaff, Tom and Waser, Rainer and Dittmann, Regina and
                      Wouters, Dirk},
      title        = {{N}euromorphic sequence learning with memristive in-memory
                      computing},
      reportid     = {FZJ-2025-00835},
      year         = {2024},
      abstract     = {Information processing in the neo-cortex happens in a
                      sequential manner and sequence learning is considered a key
                      functionality of the human brain. Even though there are
                      machine learning solutions to these problems, they, unlike
                      the biological brain, often require large amounts of
                      training data and suffer from a high energy consumption.
                      Therefore, in this project we take the neuromorphic approach
                      of bringing the biological principles of sparse neural
                      activity and in-memory computing into electronic hardware.
                      Thereby, our goal is to achieve a robust and
                      energy-efficient solution for sequence learning.The
                      Hierarchical Temporal Memory[1] concept and its biologically
                      plausible version, SpikingTM[2], describe a possible
                      algorithm for sequence learning in the neo-cortex. We prove
                      that this algorithm can operate with memristive synapses[3].
                      Memristive devices are an emerging non-volatile memory and a
                      prominent candidate for in-memory computing substrates. In
                      order to fully leverage the possibilities of these devices,
                      we adapt the SpikingTM algorithm and create a complete
                      analog / mixed signal system model around a synaptic array
                      of memristive devices[4]. We demonstrate sequence learning
                      by sparse neural activity and showcase that the use of
                      memristive devices leads to a significant gain of energy
                      efficiency. Lastly, we validate the system with real
                      memristive synaptic arrays on a custom nanometer CMOS
                      demonstrator chip by performing complex sequence learning
                      tasks with our memristive algorithm (MemSpikingTM) on
                      hardware[5].This project shows the complete neuromorphic
                      journey from a bio-plausible algorithm for the brain
                      functionality over a hardware-aware adaption for emerging
                      memristive device technology and a complete system model to
                      a successful hardware demonstration. We showcase that by
                      combining the biological principles of sparse activity and
                      connectivity with a memristive in-memory computing
                      substrate, we can fulfil the promise of robust brain-like
                      functionality and energy efficiency.},
      month         = {Jun},
      date          = {2024-06-12},
      organization  = {Helmholtz AI Conference, Düsseldorf
                       (Germany), 12 Jun 2024 - 14 Jun 2024},
      subtyp        = {Other},
      cin          = {PGI-14},
      cid          = {I:(DE-Juel1)PGI-14-20210412},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / 5233 -
                      Memristive Materials and Devices (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-HGF)POF4-5233},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1037670},
}