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@PHDTHESIS{Staudigl:1005679,
      author       = {Staudigl, Felix},
      othercontributors = {Leupers, Rainer and Krstić, Miloš},
      title        = {{T}owards trustworthy neuromorphic computing: an analysis
                      of hardware security and reliability risks},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {RWTH Aachen University},
      reportid     = {RWTH-2025-01929},
      pages        = {1 Online-Ressource : Illustrationen},
      year         = {2025},
      note         = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
                      University; Dissertation, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2025},
      abstract     = {The von Neumann bottleneck poses a significant barrier,
                      limiting the computing performance and energy efficiency of
                      conventional computing systems. Neuromorphic computing seeks
                      to overcome this bottleneck by leveraging the intrinsic
                      parallelism and efficiency of the brain-inspired
                      Computing-in-Memory (CIM) paradigm. Nonetheless, the
                      immature state of the foundational building block,
                      memristive devices, introduces substantial challenges in
                      terms of their reliability and vulnerability to hardware
                      security threats, necessitating thorough analysis to ensure
                      the development of secure and reliable computing systems for
                      future applications. To provide an in-depth investigation of
                      the reliability concerns, a fault injection platform is
                      introduced evaluating the robustness of digital CIM
                      operations. This platform marks a significant contribution
                      to understanding and mitigating reliability issues in
                      memristor-based systems by providing a holistic simulation
                      framework operating on the crossbar and operational level.
                      Moreover, the work presents NeuroHammer, a novel hardware
                      security attack exploiting the unique properties of
                      memristive crossbar arrays to compromise the integrity of
                      neuromorphic computing systems. This attack underscores the
                      susceptibility of Resistive Random-Access Memories (ReRAMs)
                      to hardware security threats, highlighting the critical need
                      for effective countermeasures. To perform a detailed
                      reliability evaluation on actual devices, this dissertation
                      unveils the NeuroBreakoutBoard (NBB)—a highly versatile
                      instrumentation platform designed to examine the effects of
                      memristive device nonidealities across various abstraction
                      levels. The NBB’s ability in executing CIM operations on
                      real memristive crossbar arrays sets it apart from
                      previously proposed platforms, emphasizing its essential
                      role in facilitating thorough analyses for the advancement
                      of dependable neuromorphic computing platforms. In
                      conclusion, this thesis delivers a comprehensive evaluation
                      of memristive devices, spanning from reliability issues to
                      hardware security threats, thereby paving the way for their
                      broad adoption and integration into the next era of
                      computing systems.},
      cin          = {611910},
      ddc          = {621.3},
      cid          = {$I:(DE-82)611910_20140620$},
      pnm          = {BMBF 16ME0400 - Verbundprojekt: Neuro-inspirierte
                      Technologien der künstlichen Intelligenz für die
                      Elektronik der Zukunft - NEUROTEC II - (16ME0400)},
      pid          = {G:(BMBF)16ME0400},
      typ          = {PUB:(DE-HGF)11},
      doi          = {10.18154/RWTH-2025-01929},
      url          = {https://publications.rwth-aachen.de/record/1005679},
}