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Found 4 papers, 2 papers with code
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training, to allow training larger models or to accelerate training.
A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression
In this paper, we propose a novel memory-driven high performance DNN training framework that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger networks.
TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications
TensorFI is a configurable FI tool that is flexible, easy to use, and portable.
A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction
The adoption of deep neural networks (DNNs) in safety-critical domains has engendered serious reliability concerns.
