Fault-aware scaling, tile reordering, and fine-tuning restore near-original accuracy for many single stuck-at-bit faults in systolic-array neural network accelerators, in simulation.
Training in turmoil: Silent data corruption in systems at scale,
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Algorithmic Strategies for Sustainable Reuse of Neural Network Accelerators with Permanent Faults
Fault-aware scaling, tile reordering, and fine-tuning restore near-original accuracy for many single stuck-at-bit faults in systolic-array neural network accelerators, in simulation.