#Float model / floating-point model

The floating-point models that meet quantized awareness training requirements.

#Calibration

The process of obtaining quantitative parameters using calibration data.

#Calibration model

Pseudo-quantized model obtained after Calibration.

#QAT / quantized awareness training

Training for quantized awareness.

#QAT model

Pseudo-quantized models obtained after quantized awareness training.

#Pseudo-quantization

The process of first quantizing and then dequantizing floating-point data which is generally implemented in network models through pseudo-quantized nodes.

#Pseudo-quantized model

Models with pseudo-quantized nodes which are typically obtained by Calibration or QAT.

#Quantized model / fixed-point model / quantized model

Convert the floating-point parameters in a pseudo-quantized model to fixed-point parameters through parameter transformations, and convert the floating-point operators to fixed-point operators, the transformed model is called a Quantized model or fixed-point model or quantized model.

#Hbir model

Models exported for deployment, typically exported from a QAT model, can be used for accuracy simulation and compilation on boards.

#Nash

Name of the BPU architecture.

#J6

Name of the processor.

#Correspondence between BPU Architecture and Processor