% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@PHDTHESIS{Trensch:1043677,
      author       = {Trensch, Guido},
      title        = {{D}evelopment and {E}valuation of {A}rchitecture {C}oncepts
                      for a {S}ystem-on-{C}hip {B}ased {N}euromorphic {C}ompute
                      {N}ode for {A}ccelerated and {R}eproducible {S}imulations of
                      {S}piking {N}eural {N}etworks in {N}euroscience},
      volume       = {71},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2025-02975},
      isbn         = {978-3-95806-832-2},
      series       = {Schriften des Forschungszentrums Jülich IAS Series},
      pages        = {263 p.},
      year         = {2025},
      note         = {Dissertation, RWTH Aachen University, 2024},
      abstract     = {Despite the great strides neuroscience has made in recent
                      decades, the underlying principles of brain function remain
                      largely unknown. Advancing the field strongly depends on the
                      ability to study large-scale neural networks and perform
                      complex simulations. Simulations in hyper-real time are of
                      high interest here, as they would enable both comprehensive
                      parameter scans and the study of slow processes such as
                      learning and long-term memory. Not even the fastest
                      supercomputer available today is capable of meeting the
                      challenge of accurate and reproducible simulation with
                      hyper-real acceleration. The development of novel
                      neuromorphic computing architectures holds out promise, but
                      the high costs and long development cycles for
                      application-specific hardware solutions makes it difficult
                      to keep pace with the rapid developments in neuroscience.
                      Commercial off-the-shelf System-on-Chip (SoC) devices,
                      integrating programmable logic, general-purpose processors,
                      and memory in a single chip, offer an alternative. This
                      technology is providing interesting new design possibilities
                      for application-specific implementations while avoiding
                      costly chip development. The primary aim of this thesis is
                      to develop and evaluate a novel SoC-based architecture for a
                      neuromorphic compute node intended to operate in a
                      multi-node cluster configuration and capable of performing
                      hyper-real-time simulations. As a complementary, yet
                      distinct approach to the neuromorphic developments aiming at
                      brain-inspired and highly efficient novel computing
                      architectures for solving real-world tasks, the design of
                      the compute node is strictly driven by neuroscience
                      requirements. These requirements are demanding, as is the
                      process of deriving appropriate design decisions from them.
                      Even for domain experts, it is often difficult to judge the
                      correctness of a simulation result. This leaves some
                      uncertainty when making design decisions and proving the
                      correctness of an architectural design and its physical
                      implementation. Methods for building credibility, such as
                      verification and validation, have been developed but are not
                      yet well established in the field of neural network modeling
                      and simulation. This thesis therefore also outlines a
                      rigorous model substantiation methodology for increasing the
                      correctness of neural network simulation results in the
                      absence of experimental validation data. The method was
                      applied during the development and evaluation of the
                      neuromorphic compute node to build credibility on
                      implementation correctness. Finally, with the goal of
                      large-scale neuromorphic computing, related technological
                      aspects are discussed and architectural enhancements for the
                      neuromorphic compute node are presented. This is accompanied
                      by a workload analysis of two large-scale neural network
                      models used in neuroscience. Also, a concept for system
                      integration is proposed that incorporates the
                      high-performance computing (HPC) landscape and takes into
                      account existing tools and workflows for modeling and
                      simulation in computational neuroscience. The results
                      presented in this thesis reveal the potential of commercial
                      off-the-shelf SoC technology and demonstrate its suitability
                      as a substrate for neuromorphic computing for application in
                      computational neuroscience. Recent developments in this
                      technology, particularly the integration of high-bandwidth
                      memory (HBM), promise significant performance improvements.
                      Acceleration factors on the order of 100 become within
                      reach, even for the simulation of large-scale spiking neural
                      networks.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5122 - Future
                      Computing $\&$ Big Data Systems (POF4-512) / SLNS - SimLab
                      Neuroscience (Helmholtz-SLNS) / ACA - Advanced Computing
                      Architectures (SO-092) / JL SMHB - Joint Lab Supercomputing
                      and Modeling for the Human Brain (JL SMHB-2021-2027) / HBP
                      SGA1 - Human Brain Project Specific Grant Agreement 1
                      (720270) / HBP SGA2 - Human Brain Project Specific Grant
                      Agreement 2 (785907) / DFG project G:(GEPRIS)491111487 -
                      Open-Access-Publikationskosten / 2025 - 2027 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5122 /
                      G:(DE-Juel1)Helmholtz-SLNS / G:(DE-HGF)SO-092 /
                      G:(DE-Juel1)JL SMHB-2021-2027 / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907 / G:(GEPRIS)491111487},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2508051223027.013713545346},
      doi          = {10.34734/FZJ-2025-02975},
      url          = {https://juser.fz-juelich.de/record/1043677},
}