CKFO: Convolution Kernel First Operated Algorithm with Applications in Memristor-based Convolutional Neural Network

Wen, S; Chen, J; Wu, Y; Yan, Z; Cao, Y; Yang, Y; Huang, T

CKFO: Convolution Kernel First Operated Algorithm with Applications in Memristor-based Convolutional Neural Network

Wen, S

Chen, J Wu, Y Yan, Z

Cao, Y Yang, Y Huang, T

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computer - Aided Design of Integrated Circuits and Systems, 2021, 40, (8), pp. 1640-1647
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Chen, J
dc.contributor.author	Wu, Y
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100
dc.contributor.author	Cao, Y
dc.contributor.author	Yang, Y
dc.contributor.author	Huang, T
dc.date.accessioned	2023-03-13T11:56:04Z
dc.date.available	2023-03-13T11:56:04Z
dc.date.issued	2021
dc.identifier.citation	IEEE Transactions on Computer - Aided Design of Integrated Circuits and Systems, 2021, 40, (8), pp. 1640-1647
dc.identifier.issn	0278-0070
dc.identifier.issn	1937-4151
dc.identifier.uri	http://hdl.handle.net/10453/167156
dc.description.abstract	IEEE This paper presents a new convolution algorithm: Convolution Kernel First Operated (CKFO), which can solve the problem that the actual calculation is not reduced after pruning the weight of the convolution neural network. According to the convolution algorithm, this paper proposes a simulated memristor implementation of a Convolutional Neural Network (CNN). After that, we use the method of ex-situ training to train CNN in Tensorflow and then download the trained parameters to the Simulink system by compiling the conductance value of memristor to test the proposed simulation model. Finally, the effectiveness of the proposed model is verified. In addition, we prune the weights of CNN and retrain it, then adjust the simulation model according to the parameters after being pruned. We are surprised to find that the convolution layer designed according to the new convolution algorithm can apply the results of the pruned weight without any modification to the circuit, which is very cumbersome in other memristor-based CNN because the distribution of the pruned weight is irregular. The parameters are reduced by 75.24% and the number of multiplication operations in the convolution layer was reduced by 30.1%, while the accuracy is just reduced by 0.06%.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation	National Natural Science Foundation of China61673187
dc.relation.ispartof	IEEE Transactions on Computer - Aided Design of Integrated Circuits and Systems
dc.relation.isbasedon	10.1109/TCAD.2020.3019993
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering, 1006 Computer Hardware
dc.subject.classification	Computer Hardware & Architecture
dc.title	CKFO: Convolution Kernel First Operated Algorithm with Applications in Memristor-based Convolutional Neural Network
dc.type	Journal Article
utslib.citation.volume	40
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1006 Computer Hardware
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2023-08-01T00:00:00+1000Z
dc.date.updated	2023-03-13T11:56:02Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	40
utslib.citation.issue	8

Abstract:

IEEE This paper presents a new convolution algorithm: Convolution Kernel First Operated (CKFO), which can solve the problem that the actual calculation is not reduced after pruning the weight of the convolution neural network. According to the convolution algorithm, this paper proposes a simulated memristor implementation of a Convolutional Neural Network (CNN). After that, we use the method of ex-situ training to train CNN in Tensorflow and then download the trained parameters to the Simulink system by compiling the conductance value of memristor to test the proposed simulation model. Finally, the effectiveness of the proposed model is verified. In addition, we prune the weights of CNN and retrain it, then adjust the simulation model according to the parameters after being pruned. We are surprised to find that the convolution layer designed according to the new convolution algorithm can apply the results of the pruned weight without any modification to the circuit, which is very cumbersome in other memristor-based CNN because the distribution of the pruned weight is irregular. The parameters are reduced by 75.24% and the number of multiplication operations in the convolution layer was reduced by 30.1%, while the accuracy is just reduced by 0.06%.

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http://hdl.handle.net/10453/167156