Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks(DNNs)in high efficiency and accuracy.This exploration implies heavy workloads for domain expert...Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks(DNNs)in high efficiency and accuracy.This exploration implies heavy workloads for domain experts,and an automatic compression method is needed.However,the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios.In this paper,we propose an end-to-end framework named AutoQNN,for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor.AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques:quantizing scheme search(QSS),quantizing precision learning(QPL),and quantized architecture generation(QAG).QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search,and then uses the Differentiable Neural Architecture Search(DNAS)algorithm to seek the layer-or model-desired scheme from the set.QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes,to the best of our knowledge.QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint.QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention,to facilitate end-to-end neural network quantization.We have implemented AutoQNN and integrated it into Keras.Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization.For 2-bit weight and activation of AlexNet and ResNet18,AutoQNN can achieve the accuracy results of 59.75%and 68.86%,respectively,and obtain accuracy improvements by up to 1.65%and 1.74%,respectively,compared with state-of-the-art methods.Especially,compared with the full-precision AlexNet and ResN展开更多
Self-assembly of Zn（NO3）2·6H2O and 2,3,5,6-tetrabromoterephthalic acid（H2TBTA） gave rise to two new zinc metal-organic frameworks,Zn（HTBTA）2（phen）2·H2O（1） and Zn（TBTA）1/2（μ2-OH）（H2O）·0....Self-assembly of Zn（NO3）2·6H2O and 2,3,5,6-tetrabromoterephthalic acid（H2TBTA） gave rise to two new zinc metal-organic frameworks,Zn（HTBTA）2（phen）2·H2O（1） and Zn（TBTA）1/2（μ2-OH）（H2O）·0.25EtOH（2）.Complex 1 is a mononuclear molecule.The hydrogen bonding interactions further connect the mononuclear molecules to generate a 2D supramolecular architecture.Complex 2 is a 2D organic-inorganic hybrid layer framework constructed from 1D rod-shaped secondary building units.展开更多
基金supported by the China Postdoctoral Science Foundation under Grant No.2022M721707the National Natural Science Foundation of China under Grant Nos.62002175 and 62272248+1 种基金the Special Funding for Excellent Enterprise Technology Correspondent of Tianjin under Grant No.21YDTPJC00380the Open Project Foundation of Information Security Evaluation Center of Civil Aviation,Civil Aviation University of China,under Grant No.ISECCA-202102.
文摘Exploring the expected quantizing scheme with suitable mixed-precision policy is the key to compress deep neural networks(DNNs)in high efficiency and accuracy.This exploration implies heavy workloads for domain experts,and an automatic compression method is needed.However,the huge search space of the automatic method introduces plenty of computing budgets that make the automatic process challenging to be applied in real scenarios.In this paper,we propose an end-to-end framework named AutoQNN,for automatically quantizing different layers utilizing different schemes and bitwidths without any human labor.AutoQNN can seek desirable quantizing schemes and mixed-precision policies for mainstream DNN models efficiently by involving three techniques:quantizing scheme search(QSS),quantizing precision learning(QPL),and quantized architecture generation(QAG).QSS introduces five quantizing schemes and defines three new schemes as a candidate set for scheme search,and then uses the Differentiable Neural Architecture Search(DNAS)algorithm to seek the layer-or model-desired scheme from the set.QPL is the first method to learn mixed-precision policies by reparameterizing the bitwidths of quantizing schemes,to the best of our knowledge.QPL optimizes both classification loss and precision loss of DNNs efficiently and obtains the relatively optimal mixed-precision model within limited model size and memory footprint.QAG is designed to convert arbitrary architectures into corresponding quantized ones without manual intervention,to facilitate end-to-end neural network quantization.We have implemented AutoQNN and integrated it into Keras.Extensive experiments demonstrate that AutoQNN can consistently outperform state-of-the-art quantization.For 2-bit weight and activation of AlexNet and ResNet18,AutoQNN can achieve the accuracy results of 59.75%and 68.86%,respectively,and obtain accuracy improvements by up to 1.65%and 1.74%,respectively,compared with state-of-the-art methods.Especially,compared with the full-precision AlexNet and ResN
基金Supported by the NNSFC (20701025)the NSF of Shandong Province (Y2008B01),and Shandong University
文摘Self-assembly of Zn（NO3）2·6H2O and 2,3,5,6-tetrabromoterephthalic acid（H2TBTA） gave rise to two new zinc metal-organic frameworks,Zn（HTBTA）2（phen）2·H2O（1） and Zn（TBTA）1/2（μ2-OH）（H2O）·0.25EtOH（2）.Complex 1 is a mononuclear molecule.The hydrogen bonding interactions further connect the mononuclear molecules to generate a 2D supramolecular architecture.Complex 2 is a 2D organic-inorganic hybrid layer framework constructed from 1D rod-shaped secondary building units.
