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.
@article{arxiv.2511.08381,
title = {Fault Tolerant Reconfigurable ML Multiprocessor},
author = {Tangrui Li and Justin Y. Shi and Matteo Spatola and Hongzheng Wang},
journal= {arXiv preprint arXiv:2511.08381},
year = {2025}
}