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Found 7 papers, 1 papers with code
Softmax Bias Correction for Quantized Generative Models
Post-training quantization (PTQ) is the go-to compression technique for large generative models, such as stable diffusion or large language models.
QBitOpt: Fast and Accurate Bitwidth Reallocation during Training
By combining fast-to-compute sensitivities with efficient solvers during QAT, QBitOpt can produce mixed-precision networks with high task performance guaranteed to satisfy strict resource constraints.
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices
We perform extensive experiments on standard FL benchmarks to evaluate our proposed FedAvg variants for quantization robustness and provide a convergence analysis for our Quantization-Aware variants in FL.
Overcoming Oscillations in Quantization-Aware Training
These effects are particularly pronounced in low-bit ($\leq$ 4-bits) quantization of efficient networks with depth-wise separable layers, such as MobileNets and EfficientNets.
Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)
chapter 4) and quantization-aware training (QAT, cf.
A White Paper on Neural Network Quantization
Neural network quantization is one of the most effective ways of achieving these savings but the additional noise it induces can lead to accuracy degradation.
In-Hindsight Quantization Range Estimation for Quantized Training
Quantization techniques applied to the inference of deep neural networks have enabled fast and efficient execution on resource-constraint devices.
