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@INPROCEEDINGS{Psychou:1043655,
      author       = {Psychou, Georgia and Zossimova, Ekaterina and Trensch,
                      Guido and Diaz, Sandra},
      title        = {{N}euromorphic computing: {B}rain-inspired concepts,
                      platforms, and their role in {HPC}},
      reportid     = {FZJ-2025-02959},
      year         = {2025},
      abstract     = {Neuromorphic computing, inspired by the brain’s structure
                      and functionality, seeks to replicate the brain’s
                      efficiency and adaptability in computational systems. At its
                      core, neuromorphic computing relies on spiking neural
                      networks (SNNs), where computation occurs through neurons
                      that accumulate charge over time and fire spikes once a
                      threshold is reached. These networks communicate
                      asynchronously, transmitting information through discrete
                      events, making them inherently suited for tasks that require
                      energy-efficient, real-time, and dynamic processing. Unlike
                      traditional artificial neural networks (ANNs), SNNs rely on
                      the temporal dynamics of spikes and charge accumulation to
                      process and communicate information.A wide variety of
                      neuromorphic platforms, both digital and analog, enable
                      experimentation with brain-inspired networks. Platforms such
                      as SpiNNaker, TrueNorth, Loihi, and BrainScaleS have been
                      pivotal in advancing neuromorphic research, but there is a
                      growing range of systems—spanning from small-scale edge
                      applications to large-scale supercomputing environments.
                      These platforms allow researchers to explore diverse
                      applications, from real-time robotics to large-scale
                      computational tasks, leveraging the unique capabilities of
                      SNNs for energy-efficient, parallel, and adaptive
                      computation. While these platforms have been pivotal in
                      advancing neuromorphic computing, their potential within
                      high-performance computing (HPC) remains largely untapped
                      and underexplored.This poster explores the emerging
                      synergies between neuromorphic computing and HPC, focusing
                      on how the unique characteristics of SNNs can be leveraged
                      to enhance computational models and workflows. Neuromorphic
                      systems offer substantial advantages in energy efficiency,
                      parallel processing, and real-time learning, which are
                      particularly beneficial for complex tasks like brain
                      simulations, AI inference, and optimization problems. The
                      ability of neuromorphic systems to adapt and learn from
                      real-time data presents exciting opportunities for advancing
                      both specialized and general-purpose HPC
                      applications.Additionally, we examine the integration of
                      neuromorphic computing within modular supercomputing
                      architectures (MSA). By incorporating neuromorphic modules
                      alongside traditional HPC clusters, these hybrid systems
                      could address challenges in scalability, energy consumption,
                      and computational flexibility. For example, neuromorphic
                      systems could assist in the emulation of quantum algorithms,
                      enhance neural network-based applications like drug
                      discovery, and support the development of future quantum
                      computing frameworks.Despite these promising applications,
                      the full integration of neuromorphic computing into HPC
                      remains a significant challenge. Advancements in hardware,
                      software frameworks, and integration with existing HPC
                      systems are critical for unlocking the full potential of
                      these technologies. As the field progresses, overcoming
                      these challenges will enable neuromorphic computing to drive
                      innovations in energy-efficient, adaptive, and large-scale
                      computations, offering transformative solutions for the
                      future of high-performance computing.},
      month         = {Jun},
      date          = {2025-06-10},
      organization  = {ISC High Performance 2025, Hamburg
                       (Germany), 10 Jun 2025 - 13 Jun 2025},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / JL SMHB - Joint Lab
                      Supercomputing and Modeling for the Human Brain (JL
                      SMHB-2021-2027) / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1043655},
}