47 citations · 103 across the 7 of their papers we have counts for
7 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…
Speaker Diaphragm Excursion Prediction: deep attention and online adaptation
Yuwei Ren, Matt Zivney, Yin Huang +3
Speaker protection algorithm is to leverage the playback signal properties to prevent over excursion while maintaining maximum loudness, especially for the mobile phone with tiny l…
FP8 versus INT8 for efficient deep learning inference
Mart van Baalen, Andrey Kuzmin, Suparna S Nair +8
Recently, the idea of using FP8 as a number format for neural network training has been floating around the deep learning world. Given that most training is currently conducted wit…
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…
Neural Network Quantization with AI Model Efficiency Toolkit (AIMET)
Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel +3
While neural networks have advanced the frontiers in many machine learning applications, they often come at a high computational cost. Reducing the power and latency of neural netw…
QKD: Quantization-aware Knowledge Distillation
Jangho Kim, Yash Bhalgat, Jinwon Lee +2
Quantization and Knowledge distillation (KD) methods are widely used to reduce memory and power consumption of deep neural networks (DNNs), especially for resource-constrained edge…