Ternary Quantization: A Survey
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.
Code (0)
등록된 구현이 없습니다.
Tasks
QuantizationSurveyMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Smart Ternary Quantization
Neural network models are resource hungry. Low bit quantization such as binary and ternary quantization is a common approach to alleviate this resource requirements. Ternary quantization provides a more flexible model an…
image-classificationImage ClassificationQuantizationHyperspherical Loss-Aware Ternary Quantization
Most of the existing works use projection functions for ternary quantization in discrete space. Scaling factors and thresholds are used in some cases to improve the model accuracy. However, the gradients used for optimiz…
image-classificationImage Classificationobject-detectionObject Detection+1Trained Ternary Quantization
Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with limited power budgets. To solve this proble…
QuantizationPruning Ternary Quantization
Inference time, model size, and accuracy are three key factors in deep model compression. Most of the existing work addresses these three key factors separately as it is difficult to optimize them all at the same time. F…
image-classificationImage ClassificationModel Compressionobject-detection+2Adaptive Binary-Ternary Quantization
Neural network models are resource hungry. It is difficult to deploy such deep networks on devices with limited resources, like smart wearables, cellphones, drones, and autonomous vehicles. Low bit quantization such as b…
Autonomous VehiclesImage ClassificationQuantization