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Found 2 papers, 1 papers with code
Adaptive Test-Time Augmentation for Low-Power CPU
Convolutional Neural Networks (ConvNets) are trained offline using the few available data and may therefore suffer from substantial accuracy loss when ported on the field, where unseen input patterns received under unpredictable external conditions can mislead the model.
EAST: Encoding-Aware Sparse Training for Deep Memory Compression of ConvNets
The implementation of Deep Convolutional Neural Networks (ConvNets) on tiny end-nodes with limited non-volatile memory space calls for smart compression strategies capable of shrinking the footprint yet preserving predictive accuracy.
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