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09/04/2023
Softmax Bias Correction for Quantized Generative Models
Post-training quantization (PTQ) is the go-to compression technique for ...
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08/18/2023
ResQ: Residual Quantization for Video Perception
This paper accelerates video perception, such as semantic segmentation a...
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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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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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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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11/30/2022
Quadapter: Adapter for GPT-2 Quantization
Transformer language models such as GPT-2 are difficult to quantize beca...
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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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06/22/2022
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices
Federated Learning (FL) is a machine learning paradigm to distributively...
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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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12/21/2021
Implicit Neural Video Compression
We propose a method to compress full-resolution video sequences with imp...
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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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05/10/2021
In-Hindsight Quantization Range Estimation for Quantized Training
Quantization techniques applied to the inference of deep neural networks...
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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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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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06/11/2019
