Kneron (taiwan) Co., Ltd.

Yunhan Ma

Jie Wu

Chun-Chen Liu

Bike Xie

Junjie Su

Hsiang-Tsun Li

After inputting input data to a floating pre-trained convolution neural network to generate floating feature maps for each layer of the floating pre-trained CNN model, a statistical analysis on the floating feature maps is performed to generate a dynamic quantization range for each layer of the floating pre-trained CNN model. Based on the obtained quantization range for each layer, the proposed quantization methodologies quantize the floating pre-trained CNN model to generate the scalar factor of each layer and the fractional bit-width of a quantized CNN model. It enables the inference engine performs low-precision fixed-point arithmetic operations to generate a fixed-point inferred CNN model.

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San Diego, CA, United States of America

Yunhan Ma