7 citations · 8 across the 3 of their papers we have counts for
3 papers
How to Parameterize Asymmetric Quantization Ranges for Quantization-Aware Training
Jaeseong You, Minseop Park, Kyunggeun Lee +3
This paper investigates three different parameterizations of asymmetric uniform quantization for quantization-aware training: (1) scale and offset, (2) minimum and maximum, and (3)…
Softmax Bias Correction for Quantized Generative Models
Nilesh Prasad Pandey, Marios Fournarakis, Chirag Patel +1
Post-training quantization (PTQ) is the go-to compression technique for large generative models, such as stable diffusion or large language models. PTQ methods commonly keep the so…
A Practical Mixed Precision Algorithm for Post-Training Quantization
Nilesh Prasad Pandey, Markus Nagel, Mart van Baalen +3
Neural network quantization is frequently used to optimize model size, latency and power consumption for on-device deployment of neural networks. In many cases, a target bit-width…