Changzhi Dai

Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-Point

Model quantization has emerged as a mandatory technique for efficient inference with advanced Deep Neural Networks (DNN). It converts the model parameters in full precision (32-bit floating point) to the hardware friendly data representation with shorter bit-width, to not only reduce the model size…