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PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.
PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.
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The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. 
The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. 
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It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance -  this helps reduce compute and inference cost.
It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance -  this helps reduce compute and inference cost.
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Code: 
Code: 
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https://github.com/Jaykef/ai-algorithms
https://github.com/Jaykef/ai-algorithms
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Paper: 
Paper: 
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https://arxiv.org/pdf/2301.13142
https://arxiv.org/pdf/2301.13142
PyTorch implementation of the Self-Compression & Differentiable Quantization Algorithm introduced in “Self-Compressing Neural Networks” paper.

The algorithm shows dynamic neural network compression during training - with reduced size of weight, activation tensors and bits required to represent weights. 

It’s basically shrinking the neural network size (weights and activations) as it’s being trained without compromising performance -  this helps reduce compute and inference cost.

Code: https://github.com/Jaykef/ai-algorithms
Paper: https://arxiv.org/pdf/2301.13142
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