With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attra...With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.展开更多
In this study,application of the spectral representation method for generation of endurance time excitation functions is introduced.Using this method,the intensifying acceleration time series is generated so that its ...In this study,application of the spectral representation method for generation of endurance time excitation functions is introduced.Using this method,the intensifying acceleration time series is generated so that its acceleration response spectrum in any desired time duration is compatible with a time-scaled predefined acceleration response spectrum.For this purpose,simulated stationary acceleration time series is multiplied by the time dependent linear modulation function,then using a simple iterative scheme,it is forced to match a target acceleration response spectrum.It is shown that the generated samples have excellent conformity in low frequency,which is useful for nonlinear endurance time analysis.In the second part of this study,it is shown that this procedure can be extended to generate a set of spatially correlated endurance time excitation functions.This makes it possible to assess the performance of long structures under multi-support seismic excitation using endurance time analysis.展开更多
Objective:To clarify the effect of endurance training on the expression profile of circRNA-lncRNA-miRNA-mRNA in myocardial tissues of mice after exhaustive exercise.Methods:A total of 45 male C57BL/6 mice were randoml...Objective:To clarify the effect of endurance training on the expression profile of circRNA-lncRNA-miRNA-mRNA in myocardial tissues of mice after exhaustive exercise.Methods:A total of 45 male C57BL/6 mice were randomly divided into control(C),low-strength endurance training(LSET)and high-strength endurance training(HSET)groups(n=15).The mice in the control group were not conducted to platform training.The mice in the LSET and HSET groups were conducted to platform training at 30%and 60%of exhaustive exercise once a day for 5 days a week,respectively.The exhaustion exercise was performed after 5 weeks of platform training.Total RNA was extracted from myocardial tissues,and the expression profile of circRNA-lncRNA-miRNA-mRNA in myocardial tissues was analyzed using Illimina transcriptome sequencing.Results:The distance and time of exhaustive exercise were longer in the LSET and HSET groups than in the control group,and the distance and time of exhaustive exercise were longer in the HSET group than in the LSET group(P<0.05).A total of 54 differentially expressed circRNAs(28 down-regulated and 26 up-regulated),7 differentially expressed lncRNAs(all down-regulated),3 differentially expressed miRNAs(1 down-regulated and 2 up-regulated)and 99 differentially expressed mRNAs(81 down-regulated and 18 up-regulated)were identified by transcriptome sequencing(P<0.05).Interaction network analysis revealed that ENSMUSG00000113041,MSTRG.79740,mmu-miR-374c-5p,18 down-regulated mRNAs and 3 up-regulated mRNAs formed a regulatory network.GO functional analysis revealed that the differentially expressed mRNAs were mainly enriched in primary metabolic processes and macromolecular synthesis and metabolic processes.KEGG pathway analysis revealed that the differentially expressed mRNAs were mainly enriched in complement and coagulation cascade pathways,estrogen signaling pathway and glucagon signaling pathway.Conclusion:Endurance training could alter the expression profile of circRNA-lncRNA-miRNA-mRNA in myocardial tissues of mice after exhaustive exercise,and these differentially expressed RNAs form a regulatory network that affects cardiomyocyte synthesis and metabolism and thus participates in the regulation of myocardial injury.展开更多
基金This work was supported by the National Natural Science Foundation of China(Nos.62034006,92264201,and 91964105)the Natural Science Foundation of Shandong Province(Nos.ZR2020JQ28 and ZR2020KF016)the Program of Qilu Young Scholars of Shandong University.
文摘With the rapid development of machine learning,the demand for high-efficient computing becomes more and more urgent.To break the bottleneck of the traditional Von Neumann architecture,computing-in-memory(CIM)has attracted increasing attention in recent years.In this work,to provide a feasible CIM solution for the large-scale neural networks(NN)requiring continuous weight updating in online training,a flash-based computing-in-memory with high endurance(10^(9) cycles)and ultrafast programming speed is investigated.On the one hand,the proposed programming scheme of channel hot electron injection(CHEI)and hot hole injection(HHI)demonstrate high linearity,symmetric potentiation,and a depression process,which help to improve the training speed and accuracy.On the other hand,the low-damage programming scheme and memory window(MW)optimizations can suppress cell degradation effectively with improved computing accuracy.Even after 109 cycles,the leakage current(I_(off))of cells remains sub-10pA,ensuring the large-scale computing ability of memory.Further characterizations are done on read disturb to demonstrate its robust reliabilities.By processing CIFAR-10 tasks,it is evident that~90%accuracy can be achieved after 109 cycles in both ResNet50 and VGG16 NN.Our results suggest that flash-based CIM has great potential to overcome the limitations of traditional Von Neumann architectures and enable high-performance NN online training,which pave the way for further development of artificial intelligence(AI)accelerators.
文摘In this study,application of the spectral representation method for generation of endurance time excitation functions is introduced.Using this method,the intensifying acceleration time series is generated so that its acceleration response spectrum in any desired time duration is compatible with a time-scaled predefined acceleration response spectrum.For this purpose,simulated stationary acceleration time series is multiplied by the time dependent linear modulation function,then using a simple iterative scheme,it is forced to match a target acceleration response spectrum.It is shown that the generated samples have excellent conformity in low frequency,which is useful for nonlinear endurance time analysis.In the second part of this study,it is shown that this procedure can be extended to generate a set of spatially correlated endurance time excitation functions.This makes it possible to assess the performance of long structures under multi-support seismic excitation using endurance time analysis.
基金The Medical and Health Science and Technology Development Planning Project of Shandong Province(202103011061)。
文摘Objective:To clarify the effect of endurance training on the expression profile of circRNA-lncRNA-miRNA-mRNA in myocardial tissues of mice after exhaustive exercise.Methods:A total of 45 male C57BL/6 mice were randomly divided into control(C),low-strength endurance training(LSET)and high-strength endurance training(HSET)groups(n=15).The mice in the control group were not conducted to platform training.The mice in the LSET and HSET groups were conducted to platform training at 30%and 60%of exhaustive exercise once a day for 5 days a week,respectively.The exhaustion exercise was performed after 5 weeks of platform training.Total RNA was extracted from myocardial tissues,and the expression profile of circRNA-lncRNA-miRNA-mRNA in myocardial tissues was analyzed using Illimina transcriptome sequencing.Results:The distance and time of exhaustive exercise were longer in the LSET and HSET groups than in the control group,and the distance and time of exhaustive exercise were longer in the HSET group than in the LSET group(P<0.05).A total of 54 differentially expressed circRNAs(28 down-regulated and 26 up-regulated),7 differentially expressed lncRNAs(all down-regulated),3 differentially expressed miRNAs(1 down-regulated and 2 up-regulated)and 99 differentially expressed mRNAs(81 down-regulated and 18 up-regulated)were identified by transcriptome sequencing(P<0.05).Interaction network analysis revealed that ENSMUSG00000113041,MSTRG.79740,mmu-miR-374c-5p,18 down-regulated mRNAs and 3 up-regulated mRNAs formed a regulatory network.GO functional analysis revealed that the differentially expressed mRNAs were mainly enriched in primary metabolic processes and macromolecular synthesis and metabolic processes.KEGG pathway analysis revealed that the differentially expressed mRNAs were mainly enriched in complement and coagulation cascade pathways,estrogen signaling pathway and glucagon signaling pathway.Conclusion:Endurance training could alter the expression profile of circRNA-lncRNA-miRNA-mRNA in myocardial tissues of mice after exhaustive exercise,and these differentially expressed RNAs form a regulatory network that affects cardiomyocyte synthesis and metabolism and thus participates in the regulation of myocardial injury.
