Deployment of Spiking Neural Networks on Multi-Node Neuromorphic Systems with Integrated Error Detection and Isolation
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| Publié dans: | ProQuest Dissertations and Theses (2025) |
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| Accès en ligne: | Citation/Abstract Full Text - PDF |
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| Résumé: | Neuromorphic computing systems implementing spiking neural networks (SNNs) offer significant advantages in energy efficiency and real-time processing for brain-inspired applications. However, deploying large-scale SNNs on neuromorphic hardware faces critical challenges in resource allocation and reliability assurance that limit their practical adoption. Conventional clustering approaches, while effective for traditional computing architectures, fail to account for the unique constraints of neuromorphic cores such as crossbar capacity limitations and synaptic mapping requirements. Moreover, the deployment of neuromorphic systems in mission-critical applications necessitates robust fault detection capabilities that current approaches do not adequately address. This dissertation presents a comprehensive framework for efficient and reliable SNN deployment on neuromorphic hardware through architecture-aware clustering and integrated fault detection mechanisms. The first major contribution is the development of architecture-aware clustering algorithms that explicitly consider crossbar constraints during SNN partitioning. Beginning with the foundational crossbar architecture, we demonstrate how synaptic-connection-aware clustering significantly improves resource utilization compared to traditional graph partitioning methods, resulting in 1.49-1.83x better resource utilization for crossbars. Building upon this foundation, the ATLAS framework addresses the performance speed and solution quality by employing specialized data structures and meta-heuristic optimizations. Our work resulted in dramatic computational improvements by reducing clustering time from over 24 hours to under 20 minutes for large networks with substantial energy consumption reductions from 12.5% to 45.7% across evaluated models. As part of this work, we also introduce a software framework that abstracts architecture-specific constraints, enabling portability across various neuromorphic backends. The next major contribution introduces a software-based built-in self-test (BIST) strategy that leverages alarm placement theory to minimize fault detection overhead while ensuring comprehensive coverage. The alarm placement algorithm is applied to SNNs trained on various datasets resulting in high test coverage across diverse fault models such as stuck-at faults and resistance drifts. Multiple methods are introduced to enable the application of alarm placement on much larger workloads. Through channel-based abstraction and trace-based analysis, we achieve complexity reductions of 32× for LeNet and 248× for AlexNet while maintaining effective fault coverage exceeding 96-98%. Together, these contributions establish a complete methodology for neuromorphic SNN deployment that addresses both efficiency and reliability requirements, providing a foundation for practical large-scale neuromorphic computing systems in safety-critical applications. |
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| ISBN: | 9798293832736 |
| Source: | ProQuest Dissertations & Theses Global |