Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of ...Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.展开更多
Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems.At the same time,the computational complexity and resource consumption of t...Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems.At the same time,the computational complexity and resource consumption of these networks continue to increase.This poses a significant challenge to the deployment of such networks,especially in real-time applications or on resource-limited devices.Thus,network acceleration has become a hot topic within the deep learning community.As for hardware implementation of deep neural networks,a batch of accelerators based on a field-programmable gate array(FPGA) or an application-specific integrated circuit(ASIC)have been proposed in recent years.In this paper,we provide a comprehensive survey of recent advances in network acceleration,compression,and accelerator design from both algorithm and hardware points of view.Specifically,we provide a thorough analysis of each of the following topics:network pruning,low-rank approximation,network quantization,teacher–student networks,compact network design,and hardware accelerators.Finally,we introduce and discuss a few possible future directions.展开更多
Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from s...Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from sharp performance drops when the pruning ratio is large,probably due to the unrecoverable information loss caused by aggressive pruning.In this paper,we propose a dual attention based pruning approach called DualPrune to push the limit of network pruning at an ultra-high compression ratio.Firstly,it adopts a graph attention network(GAT)to automatically extract filter-level and layer-level features from CNNs based on the roles of their filters in the whole computation graph.Then the extracted comprehensive features are fed to a side-attention network,which generates sparse attention weights for individual filters to guide model pruning.To avoid layer collapse,the side-attention network adopts a side-path design to preserve the information flow going through the CNN model properly,which allows the CNN model to be pruned at a high compression ratio at initialization and trained from scratch afterward.Extensive experiments based on several well-known CNN models and real-world datasets show that the proposed DualPrune method outperforms the state-of-the-art methods with significant performance improvement,particularly for model compression at a high pruning ratio.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.51275415 and50905144)the Natural Science Basic Research Plan in Shanxi Province(No.2011JQ6004)the Program of the Ministry of Education of China for Introducing Talents of Discipline to Universities(No.B08040)
文摘Warm rotary draw bending provides a feasible method to form the large-diameter thin-walled（LDTW）TC4 bent tubes, which are widely used in the pneumatic system of aircrafts. An accurate prediction of flow behavior of TC4 tubes considering the couple effects of temperature,strain rate and strain is critical for understanding the deformation behavior of metals and optimizing the processing parameters in warm rotary draw bending of TC4 tubes. In this study, isothermal compression tests of TC4 tube alloy were performed from 573 to 873 K with an interval of 100 K and strain rates of 0.001, 0.010 and0.100 s^（-1）. The prediction of flow behavior was done using two constitutive models, namely modified Arrhenius model and artificial neural network（ANN） model. The predictions of these constitutive models were compared using statistical measures like correlation coefficient（R）, average absolute relative error（AARE） and its variation with the deformation parameters（temperature, strain rate and strain）. Analysis of statistical measures reveals that the two models show high predicted accuracy in terms of R and AARE. Comparatively speaking, the ANN model presents higher predicted accuracy than the modified Arrhenius model. In addition, the predicted accuracy of ANN model presents high stability at the whole deformation parameter ranges, whereas the predictability of the modified Arrhenius model has some fluctuation at different deformation conditions. It presents higher predicted accuracy at temperatures of 573-773 K, strain rates of 0.010-0.100 s^（-1）and strain of 0.04-0.32, while low accuracy at temperature of 873 K, strain rates of 0.001 s^（-1）and strain of 0.36-0.48.Thus, the application of modified Arrhenius model is limited by its relatively low predicted accuracy at some deformation conditions, while the ANN model presents very high predicted accuracy at all deformation conditions,which can be used to study the compression behavior of TC4 tube at the temperature range of 573-873 K and the strain rate of 0.001-0.100 s^（-1）. It can provide guideline for the design of processing parameters in warm rotary draw bending of LDTW TC4 tubes.
文摘Deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems.At the same time,the computational complexity and resource consumption of these networks continue to increase.This poses a significant challenge to the deployment of such networks,especially in real-time applications or on resource-limited devices.Thus,network acceleration has become a hot topic within the deep learning community.As for hardware implementation of deep neural networks,a batch of accelerators based on a field-programmable gate array(FPGA) or an application-specific integrated circuit(ASIC)have been proposed in recent years.In this paper,we provide a comprehensive survey of recent advances in network acceleration,compression,and accelerator design from both algorithm and hardware points of view.Specifically,we provide a thorough analysis of each of the following topics:network pruning,low-rank approximation,network quantization,teacher–student networks,compact network design,and hardware accelerators.Finally,we introduce and discuss a few possible future directions.
基金supported by the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20222003the National Natural Science Foundation of China under Grant Nos.61972196,61832008,and 61832005the Collaborative Innovation Center of Novel Software Technology and Industrialization,and the Sino-German Institutes of Social Computing.
文摘Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from sharp performance drops when the pruning ratio is large,probably due to the unrecoverable information loss caused by aggressive pruning.In this paper,we propose a dual attention based pruning approach called DualPrune to push the limit of network pruning at an ultra-high compression ratio.Firstly,it adopts a graph attention network(GAT)to automatically extract filter-level and layer-level features from CNNs based on the roles of their filters in the whole computation graph.Then the extracted comprehensive features are fed to a side-attention network,which generates sparse attention weights for individual filters to guide model pruning.To avoid layer collapse,the side-attention network adopts a side-path design to preserve the information flow going through the CNN model properly,which allows the CNN model to be pruned at a high compression ratio at initialization and trained from scratch afterward.Extensive experiments based on several well-known CNN models and real-world datasets show that the proposed DualPrune method outperforms the state-of-the-art methods with significant performance improvement,particularly for model compression at a high pruning ratio.
