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Found 3 papers, 2 papers with code
How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct
Namely: (i) gradient quantization through floating-point conversion, (ii) lossless compression of the quantized gradient, and (iii) quantization error correction.
Convert, compress, correct: Three steps toward communication-efficient DNN training
In this paper, we introduce a novel algorithm, $\mathsf{CO}_3$, for communication-efficiency distributed Deep Neural Network (DNN) training.
DNN gradient lossless compression: Can GenNorm be the answer?
In this paper we argue that, for some networks of practical interest, the gradient entries can be well modelled as having a generalized normal (GenNorm) distribution.
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