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Qualcomm

A White Paper on Neural Network Quantization

While neural networks have advanced the frontiers in many applications, ...
06/15/2021 ∙ by Markus Nagel, et al. ∙ 12 ∙ share

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Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces

This work presents DONNA (Distilling Optimal Neural Network Architecture...
12/16/2020 ∙ by Bert Moons, et al. ∙ 10 ∙ share

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Differentiable Joint Pruning and Quantization for Hardware Efficiency

We present a differentiable joint pruning and quantization (DJPQ) scheme...
07/20/2020 ∙ by Ying Wang, et al. ∙ 0 ∙ share

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Bayesian Bits: Unifying Quantization and Pruning

We introduce Bayesian Bits, a practical method for joint mixed precision...
05/14/2020 ∙ by Mart van Baalen, et al. ∙ 4 ∙ share

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Up or Down? Adaptive Rounding for Post-Training Quantization

When quantizing neural networks, assigning each floating-point weight to...
04/22/2020 ∙ by Markus Nagel, et al. ∙ 1 ∙ share

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LSQ+: Improving low-bit quantization through learnable offsets and better initialization

Unlike ReLU, newer activation functions (like Swish, H-swish, Mish) that...
04/20/2020 ∙ by Yash Bhalgat, et al. ∙ 5 ∙ share

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Conditional Channel Gated Networks for Task-Aware Continual Learning

Convolutional Neural Networks experience catastrophic forgetting when op...
03/31/2020 ∙ by Davide Abati, et al. ∙ 10 ∙ share

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Learned Threshold Pruning

This paper presents a novel differentiable method for unstructured weigh...
02/28/2020 ∙ by Kambiz Azarian, et al. ∙ 6 ∙ share

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Gradient ℓ_1 Regularization for Quantization Robustness

We analyze the effect of quantizing weights and activations of neural ne...
02/18/2020 ∙ by Milad Alizadeh, et al. ∙ 14 ∙ share

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Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks

The success of deep neural networks in many real-world applications is l...
12/20/2019 ∙ by Andrey Kuzmin, et al. ∙ 47 ∙ share

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Batch-Shaped Channel Gated Networks

We present a method for gating deep-learning architectures on a fine-gra...
07/15/2019 ∙ by Babak Ehteshami Bejnordi, et al. ∙ 1 ∙ share

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Data-Free Quantization through Weight Equalization and Bias Correction

We introduce a data-free quantization method for deep neural networks th...
06/11/2019 ∙ by Markus Nagel, et al. ∙ 1 ∙ share

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Relaxed Quantization for Discretized Neural Networks

Neural network quantization has become an important research area due to...
10/03/2018 ∙ by Christos Louizos, et al. ∙ 2 ∙ share

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Tijmen Blankevoort

Featured Co-authors

Max Welling

Nojun Kwak

Rita Cucchiara

Efstratios Gavves

Ying Wang

Simone Calderara

Christos Louizos

Dushyant Mehta

Arash Behboodi

Rana Ali Amjad

Yash Bhalgat

A White Paper on Neural Network Quantization

Distilling Optimal Neural Networks: Rapid Search in Diverse Spaces

Differentiable Joint Pruning and Quantization for Hardware Efficiency

Bayesian Bits: Unifying Quantization and Pruning

Up or Down? Adaptive Rounding for Post-Training Quantization

LSQ+: Improving low-bit quantization through learnable offsets and better initialization

Conditional Channel Gated Networks for Task-Aware Continual Learning

Learned Threshold Pruning

Gradient ℓ_1 Regularization for Quantization Robustness

Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks

Batch-Shaped Channel Gated Networks

Data-Free Quantization through Weight Equalization and Bias Correction

Relaxed Quantization for Discretized Neural Networks