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07/10/2023
QBitOpt: Fast and Accurate Bitwidth Reallocation during Training
Quantizing neural networks is one of the most effective methods for achi...
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07/06/2023
Pruning vs Quantization: Which is Better?
Neural network pruning and quantization techniques are almost as old as ...
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03/31/2023
FP8 versus INT8 for efficient deep learning inference
Recently, the idea of using FP8 as a number format for neural network tr...
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02/10/2023
A Practical Mixed Precision Algorithm for Post-Training Quantization
Neural network quantization is frequently used to optimize model size, l...
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08/19/2022
FP8 Quantization: The Power of the Exponent
When quantizing neural networks for efficient inference, low-bit integer...
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07/22/2022
Quantized Sparse Weight Decomposition for Neural Network Compression
In this paper, we introduce a novel method of neural network weight comp...
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02/02/2022
Cyclical Pruning for Sparse Neural Networks
Current methods for pruning neural network weights iteratively apply mag...
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06/15/2021
A White Paper on Neural Network Quantization
While neural networks have advanced the frontiers in many applications, ...
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05/14/2020
Bayesian Bits: Unifying Quantization and Pruning
We introduce Bayesian Bits, a practical method for joint mixed precision...
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04/22/2020
Up or Down? Adaptive Rounding for Post-Training Quantization
When quantizing neural networks, assigning each floating-point weight to...
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02/18/2020
Gradient ℓ_1 Regularization for Quantization Robustness
We analyze the effect of quantizing weights and activations of neural ne...
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06/11/2019