  Inference time, model size, and accuracy are critical for deploying deep
neural network models. Numerous research efforts have been made to compress
neural network models with faster inference and higher accuracy. Pruning and
quantization are mainstream methods to this end. During model quantization,
converting individual float values of layer weights to low-precision ones can
substantially reduce the computational overhead and improve the inference
speed. Many quantization methods have been studied, for example, vector
quantization, low-bit quantization, and binary/ternary quantization. This
survey focuses on ternary quantization. We review the evolution of ternary
quantization and investigate the relationships among existing ternary
quantization methods from the perspective of projection function and
optimization methods.
