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Deep neural networks are the state-of-the-art methods for many real-worldtasks, such as computer vision, natural language processing and speechrecognition. For all its popularity, deep neural networks are also criticizedfor consuming a lot of memory and draining battery life of devices duringtraining and inference. This makes it hard to deploy these models on mobile orembedded devices which have tight resource constraints. Quantization isrecognized as one of the most effective approaches to satisfy the extremememory requirements that deep neural network models demand. Instead of adopting32-bit floating point format to represent weights, quantized representationsstore weights using more compact formats such as integers or even binarynumbers. Despite a possible degradation in predictive performance, quantizationprovides a potential solution to greatly reduce the model size and the energyconsumption. In this survey, we give a thorough review of different aspects ofquantized neural networks. Current challenges and trends of quantized neuralnetworks are also discussed.

A Survey on Methods and Theories of Quantized Neural Networks