Fault Tolerant Reconfigurable ML Multiprocessor
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912702224400384 |
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| author | Li, Tangrui Shi, Justin Y. Spatola, Matteo Wang, Hongzheng |
| author_facet | Li, Tangrui Shi, Justin Y. Spatola, Matteo Wang, Hongzheng |
| contents | This paper reports three computational experiments for a von Neumann inspired reconfigurable fault tolerant multiprocessor for neural network (NN) training workflows. The experiments are intended to prove the feasibility of the proposed reconfigurable multiprocessor architecture for non-regular workflows on robustness of adaptability. A potential integration with MLIR compilers is also discussed for integrating diverse accelerator hardware for existing practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08381 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fault Tolerant Reconfigurable ML Multiprocessor Li, Tangrui Shi, Justin Y. Spatola, Matteo Wang, Hongzheng Networking and Internet Architecture This paper reports three computational experiments for a von Neumann inspired reconfigurable fault tolerant multiprocessor for neural network (NN) training workflows. The experiments are intended to prove the feasibility of the proposed reconfigurable multiprocessor architecture for non-regular workflows on robustness of adaptability. A potential integration with MLIR compilers is also discussed for integrating diverse accelerator hardware for existing practical applications. |
| title | Fault Tolerant Reconfigurable ML Multiprocessor |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2511.08381 |