Bit-serial convolution with prediction threshold for convolutional neural networks Electrical Engineering Subject Index: EL7 Signal Processing
To reduce the implementation complexity and power consumption of the convolution operation in a convolutional neural network (CNN), this work proposes a new convolution method using the serial input and prediction threshold. To confirm the benefits of the proposed method, we use the original paramet...
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| Published in | Journal of the Chinese Institute of Engineers Vol. 45; no. 3; pp. 266 - 272 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
03.04.2022
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-3839 2158-7299 |
| DOI | 10.1080/02533839.2022.2034050 |
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| Abstract | To reduce the implementation complexity and power consumption of the convolution operation in a convolutional neural network (CNN), this work proposes a new convolution method using the serial input and prediction threshold. To confirm the benefits of the proposed method, we use the original parameters, i.e. kernel weights and biases, of the popular AlexNet to verify the proposed algorithm and then implement its digital circuit. According to implementation data and comparison with traditional convolution using bit-parallel input, the implementation gain in terms of the throughput/power/area of the serial convolution is 7.57 times that of parallel convolution. |
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| AbstractList | To reduce the implementation complexity and power consumption of the convolution operation in a convolutional neural network (CNN), this work proposes a new convolution method using the serial input and prediction threshold. To confirm the benefits of the proposed method, we use the original parameters, i.e. kernel weights and biases, of the popular AlexNet to verify the proposed algorithm and then implement its digital circuit. According to implementation data and comparison with traditional convolution using bit-parallel input, the implementation gain in terms of the throughput/power/area of the serial convolution is 7.57 times that of parallel convolution. |
| Author | Hsiao, Jen-Hao Wu, Yu-Feng Chin, Wen-Long Chang, Deng-Kai |
| Author_xml | – sequence: 1 givenname: Jen-Hao surname: Hsiao fullname: Hsiao, Jen-Hao organization: National Cheng Kung University – sequence: 2 givenname: Wen-Long surname: Chin fullname: Chin, Wen-Long email: wlchin@mail.ncku.edu.tw organization: National Cheng Kung University – sequence: 3 givenname: Yu-Feng surname: Wu fullname: Wu, Yu-Feng organization: National Cheng Kung University – sequence: 4 givenname: Deng-Kai surname: Chang fullname: Chang, Deng-Kai organization: National Cheng Kung University |
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| Cites_doi | 10.1109/MNET.011.2000096 10.1049/cmu2.12129 10.1109/MICRO.2016.7783722 10.1109/ICASSP.2017.7953288 10.1145/3065386 10.1109/ICIS.2016.7550778 10.1007/s11263-015-0816-y |
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| Copyright | 2022 The Chinese Institute of Engineers 2022 |
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| Snippet | To reduce the implementation complexity and power consumption of the convolution operation in a convolutional neural network (CNN), this work proposes a new... |
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| SubjectTerms | Convolutional neural network hardware accelerator serial input |
| Subtitle | Electrical Engineering Subject Index: EL7 Signal Processing |
| Title | Bit-serial convolution with prediction threshold for convolutional neural networks |
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