Joint Training of Low-Precision Neural Network with Quantization Interval Parameters

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Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search (2018, background): “More recent works such as Rastegari et al. (2016); Zhou et al. (2016); Choi et al. (2018); Jung et al. (2018); Zhuang et al. (2018) quantize both weights and activations to reduce the computational cost on CPUs and dedicated hardware accelerators.”
Learned Step Size Quantization (2019, methodology): “Prior approaches using backpropagation to learn quantization controlling parameters (Choi et al., 2018b;a; Jung et al., 2018) completely remove the round operation when differentiating the forward pass, equivalent in our derivation to removing the round function in equation 5, so that where |v/s| < ”
Post training 4-bit quantization of convolutional networks for rapid-deployment (2018, methodology): “These techniques, known as quantization-aware training, show significant improvement over post-training quantization (Krishnamoorthi, 2018; Choi et al., 2018; Jung et al., 2018).”
Accurate and Efficient 2-bit Quantized Neural Networks (2019, methodology): “Training Complexity Quantization at inference Enlarge Network size WEQ (Park et al., 2017) Weight clustering (iterative) Non-uniform N BQ (Zhou et al., 2016) Histogram equalization (iterative) Non-uniform N HWGQ (Cai et al., 2017) Lloyd’s algorithm (iterative) Non-uniform N LQ-Nets (Zhang et al., 20”