awesome-quantization-and-fixed-point-training
github.com/a-suozhang/awesome-quantization-and-fixed-point-training ↗Neural Network Quantization & Low-Bit Fixed Point Training For Hardware-Friendly Algorithm Design
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A. Post-Training QuantizationB. Quantize-Aware-TrainingPost-Training QuantizationQuantize-Aware TrainingOthersBinary及其延申(极低比特)量化方法（低比特）理论分析奇技淫巧Tensorflow LitePyTorch
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Installation instructions →What's inside
A. Post-Training Quantization
- 1510-Deep Compression
- 1511-Fixed Point Quantization of Deep Convolutional Network
- 1611-Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
- 1702-Incremental Quantization
- 1711-NISP: Pruning Networks using Neuron Importance Score Propagation
- 1805-Retraining-Based Iterative Weight Quantization for Deep Neural Networks
B. Quantize-Aware-Training
Binary及其延申(极低比特)
理论分析
奇技淫巧
- Analysis Of Quantized MOdels-ICLR2019
- And the Bit Goes Down: Revisiting the Quantization of Neural Networks
- Deep Learning with Low Precision by Half-wave Gaussian Quantization
- Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations
- ernGrad: Ternary Gradients to Reduce Communication in Distributed Deep LearningFixed-Point Training
- Learning low-precision neural networks without Straight-Through Estimator (STE)
Quantize-Aware Training
- Binary Neural Networks: A SurveyBinary
Half Gaussian Quantization
- Towards Unified INT8 Training for Convolutional Neural NetworkFixed-Point Training
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