Quantizer in NNIΒΆ

NNI implements the main part of the quantizaiton algorithm as quantizer. All quantizers are implemented as close as possible to what is described in the paper (if it has). The following table provides a brief introduction to the quantizers implemented in nni, click the link in table to view a more detailed introduction and use cases.


Brief Introduction of Algorithm

Naive Quantizer

Quantize weights to default 8 bits

QAT Quantizer

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. Reference Paper

DoReFa Quantizer

DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. Reference Paper

BNN Quantizer

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. Reference Paper

LSQ Quantizer

Learned step size quantization. Reference Paper

Observer Quantizer

Post training quantizaiton. Collect quantization information during calibration with observers.