001043677 001__ 1043677
001043677 005__ 20250826202235.0
001043677 020__ $$a978-3-95806-832-2
001043677 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-02975
001043677 0247_ $$2URN$$aurn:nbn:de:0001-2508051223027.013713545346
001043677 037__ $$aFZJ-2025-02975
001043677 041__ $$aEnglish
001043677 1001_ $$0P:(DE-Juel1)168379$$aTrensch, Guido$$b0$$eCorresponding author$$ufzj
001043677 245__ $$aDevelopment and Evaluation of Architecture Concepts for a System-on-Chip Based Neuromorphic Compute Node for Accelerated and Reproducible Simulations of Spiking Neural Networks in Neuroscience$$f - 2024-12-11
001043677 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2025
001043677 300__ $$a263 p.
001043677 3367_ $$2DataCite$$aOutput Types/Dissertation
001043677 3367_ $$0PUB:(DE-HGF)3$$2PUB:(DE-HGF)$$aBook$$mbook
001043677 3367_ $$2ORCID$$aDISSERTATION
001043677 3367_ $$2BibTeX$$aPHDTHESIS
001043677 3367_ $$02$$2EndNote$$aThesis
001043677 3367_ $$0PUB:(DE-HGF)11$$2PUB:(DE-HGF)$$aDissertation / PhD Thesis$$bphd$$mphd$$s1754033264_23131
001043677 3367_ $$2DRIVER$$adoctoralThesis
001043677 4900_ $$aSchriften des Forschungszentrums Jülich IAS Series$$v71
001043677 502__ $$aDissertation, RWTH Aachen University, 2024$$bDissertation$$cRWTH Aachen University$$d2024$$o2024-12-11
001043677 520__ $$aDespite 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.
001043677 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001043677 536__ $$0G:(DE-HGF)POF4-5122$$a5122 - Future Computing & Big Data Systems (POF4-512)$$cPOF4-512$$fPOF IV$$x1
001043677 536__ $$0G:(DE-Juel1)Helmholtz-SLNS$$aSLNS - SimLab Neuroscience (Helmholtz-SLNS)$$cHelmholtz-SLNS$$x2
001043677 536__ $$0G:(DE-HGF)SO-092$$aACA - Advanced Computing Architectures (SO-092)$$cSO-092$$x3
001043677 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x4
001043677 536__ $$0G:(EU-Grant)720270$$aHBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270)$$c720270$$fH2020-Adhoc-2014-20$$x5
001043677 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x6
001043677 536__ $$0G:(GEPRIS)491111487$$aDFG project G:(GEPRIS)491111487 - Open-Access-Publikationskosten / 2025 - 2027 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x7
001043677 8564_ $$uhttps://juser.fz-juelich.de/record/1043677/files/IAS_71.pdf$$yOpenAccess
001043677 909CO $$ooai:juser.fz-juelich.de:1043677$$pdnbdelivery$$pec_fundedresources$$pVDB$$pdriver$$purn$$popen_access$$popenaire
001043677 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168379$$aForschungszentrum Jülich$$b0$$kFZJ
001043677 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001043677 9131_ $$0G:(DE-HGF)POF4-512$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5122$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vSupercomputing & Big Data Infrastructures$$x1
001043677 9141_ $$y2025
001043677 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001043677 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001043677 920__ $$lyes
001043677 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001043677 980__ $$aphd
001043677 980__ $$aVDB
001043677 980__ $$aUNRESTRICTED
001043677 980__ $$abook
001043677 980__ $$aI:(DE-Juel1)JSC-20090406
001043677 9801_ $$aFullTexts