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08/14/2023
Efficient Neural PDE-Solvers using Quantization Aware Training
In the past years, the application of neural networks as an alternative ...
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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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07/05/2023
MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers
The input tokens to Vision Transformers carry little semantic meaning as...
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06/22/2023
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
Transformer models have been widely adopted in various domains over the ...
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04/11/2023
Revisiting Single-gated Mixtures of Experts
Mixture of Experts (MoE) are rising in popularity as a means to train ex...
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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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06/16/2022
Simple and Efficient Architectures for Semantic Segmentation
Though the state-of-the architectures for semantic segmentation, such as...
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03/21/2022
Overcoming Oscillations in Quantization-Aware Training
When training neural networks with simulated quantization, we observe th...
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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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01/20/2022
Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)
While neural networks have advanced the frontiers in many machine learni...
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09/27/2021
Understanding and Overcoming the Challenges of Efficient Transformer Quantization
Transformer-based architectures have become the de-facto standard models...
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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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12/16/2020
Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces
This work presents DONNA (Distilling Optimal Neural Network Architecture...
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07/20/2020
Differentiable Joint Pruning and Quantization for Hardware Efficiency
We present a differentiable joint pruning and quantization (DJPQ) scheme...
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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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04/20/2020
LSQ+: Improving low-bit quantization through learnable offsets and better initialization
Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that...
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03/31/2020
Conditional Channel Gated Networks for Task-Aware Continual Learning
Convolutional Neural Networks experience catastrophic forgetting when op...
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02/28/2020
Learned Threshold Pruning
This paper presents a novel differentiable method for unstructured weigh...
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02/18/2020
Gradient â„“_1 Regularization for Quantization Robustness
We analyze the effect of quantizing weights and activations of neural ne...
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12/20/2019
Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks
The success of deep neural networks in many real-world applications is l...
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07/15/2019
Batch-Shaped Channel Gated Networks
We present a method for gating deep-learning architectures on a fine-gra...
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06/11/2019
Data-Free Quantization through Weight Equalization and Bias Correction
We introduce a data-free quantization method for deep neural networks th...
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10/03/2018
