tinyML Talks: A Practical Guide to Neural Network Quantization

"A Practical Guide to Neural Network Quantization"
Marios Fournarakis
Deep Learning Researcher
Qualcomm AI Research, Amsterdam
Neural network quantization is an effective way of reducing the power requirements and latency of neural network inference while maintaining high accuracy. The success of quantization has led to a large volume of literature and competing methods in recent years, and Qualcomm has been at the forefront of this research. This talk aims to cut through the noise and introduce a practical guide for quantizing neural networks inspired by our research and expertise at Qualcomm. We will begin with an introduction to quantization and fixed-point accelerators for neural network inference. We will then consider implementation pipelines for quantizing neural networks with near floating-point accuracy for popular neural networks and benchmarks. Finally, you will leave this talk with a set of diagnostic and debugging tools to address common neural network quantization issues.
You can find more information about the theory and algorithms we will discuss in this talk in our White Paper on Neural Network Quantization at the following arXiv link: arxiv.org/abs/2106.08295