Neural Network Quantization with AI Model Efficiency Toolkit (AIMET). (arXiv:2201.08442v1 [cs.LG])

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 network inference is vital to integrating modern networks
into edge devices with strict power and compute requirements. 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 this white
paper, we present an overview of neural network …

ai ai model arxiv network neural network toolkit