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Found 7 papers, 3 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.
Chameleon: Adaptive Code Optimization for Expedited Deep Neural Network Compilation
This solution dubbed Chameleon leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain-knowledge inspired logic to improve the samples itself.
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.
Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation
Further experiments also confirm that our adaptive sampling can even improve AutoTVM's simulated annealing by 4. 00x.
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.
