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Found 5 papers, 1 papers with code
WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATION
We show how WaveQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization
We show how SINAREQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.
Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks
The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network.
SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training
To further mitigate this loss, we propose a novel sinusoidal regularization, called SinReQ1, for deep quantized training.
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
We show how ReLeQ can balance speed and quality, and provide an asymmetric general solution for quantization of a large variety of deep networks (AlexNet, CIFAR-10, LeNet, MobileNet-V1, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy (=< 0. 3% loss) while minimizing the computation and storage cost.
