Quantization

• A Survey of Quantization Methods for Efficient Neural Network Inference
• Post training 4-bit quantization of convolutional networks for rapid-deployment
• Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
• Up or Down? Adaptive Rounding for Post-Training Quantization
• Automated Log-Scale Quantization for Low-Cost Deep Neural Networks
• BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
• Data-Free Quantization Through Weight Equalization and Bias Correction
• Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
• Deep Learning with Limited Numerical Precision
• Loss Aware Post-training Quantization
• Learnable Companding Quantization for Accurate Low-bit Neural Networks
• LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
• Learned Step Size Quantization
• MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
• Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
• Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks
• ZeroQ: A Novel Zero Shot Quantization Framework
• NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
• PACT: Parameterized Clipping Activation for Quantized Neural Networks
• Quantization Applications
  • Post-training Quantization on Diffusion Models
  • SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
  • LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
  • Quantizable Transformers Removing Outliers by Helping Attention Heads Do Nothing
  • Q-DM: An Efficient Low-bit Quantized Diffusion Model