Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systems
Abstract Many recent research efforts have exploited data sparsity for the acceleration of convolutional neural network (CNN) inferences. However, the effects of data transfer between main memory and the CNN accelerator have been largely overlooked. In this work, the authors propose a CNN accelerati...
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Wiley
2022-01-01
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| Series: | IET Computers & Digital Techniques |
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| Online Access: | https://doi.org/10.1049/cdt2.12038 |
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| author | Jisu Kwon Joonho Kong Arslan Munir |
| author_facet | Jisu Kwon Joonho Kong Arslan Munir |
| author_sort | Jisu Kwon |
| collection | DOAJ |
| description | Abstract Many recent research efforts have exploited data sparsity for the acceleration of convolutional neural network (CNN) inferences. However, the effects of data transfer between main memory and the CNN accelerator have been largely overlooked. In this work, the authors propose a CNN acceleration technique that leverages hardware/software co‐design and exploits the sparsity in input feature maps (IFMs). On the software side, the authors' technique employs a novel lossless compression scheme for IFMs, which are sent to the hardware accelerator via direct memory access. On the hardware side, the authors' technique uses a CNN inference accelerator that performs convolutional layer operations with their compressed data format. With several design optimization techniques, the authors have implemented their technique in a field‐programmable gate array (FPGA) system‐on‐chip platform and evaluated their technique for six different convolutional layers in SqueezeNet. Results reveal that the authors' technique improves the performance by 1.1×–22.6× while reducing energy consumption by 47.7%–97.4% as compared to the CPU‐based execution. Furthermore, results indicate that the IFM size and transfer latency are reduced by 34.0%–85.2% and 4.4%–75.7%, respectively, compared to the case without data compression. In addition, the authors' hardware accelerator shows better performance per hardware resource with less than or comparable power consumption to the state‐of‐the‐art FPGA‐based designs. |
| format | Article |
| id | doaj-art-ace7e778e7cf4e7296bc44054dbbaf99 |
| institution | Kabale University |
| issn | 1751-8601 1751-861X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computers & Digital Techniques |
| spelling | doaj-art-ace7e778e7cf4e7296bc44054dbbaf992025-08-20T03:38:59ZengWileyIET Computers & Digital Techniques1751-86011751-861X2022-01-01161294310.1049/cdt2.12038Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systemsJisu Kwon0Joonho Kong1Arslan Munir2School of Electronic and Electrical Engineering Kyungpook National University Daegu South KoreaSchool of Electronic and Electrical Engineering Kyungpook National University Daegu South KoreaDepartment of Computer Science Kansas State University Manhattan Kansas USAAbstract Many recent research efforts have exploited data sparsity for the acceleration of convolutional neural network (CNN) inferences. However, the effects of data transfer between main memory and the CNN accelerator have been largely overlooked. In this work, the authors propose a CNN acceleration technique that leverages hardware/software co‐design and exploits the sparsity in input feature maps (IFMs). On the software side, the authors' technique employs a novel lossless compression scheme for IFMs, which are sent to the hardware accelerator via direct memory access. On the hardware side, the authors' technique uses a CNN inference accelerator that performs convolutional layer operations with their compressed data format. With several design optimization techniques, the authors have implemented their technique in a field‐programmable gate array (FPGA) system‐on‐chip platform and evaluated their technique for six different convolutional layers in SqueezeNet. Results reveal that the authors' technique improves the performance by 1.1×–22.6× while reducing energy consumption by 47.7%–97.4% as compared to the CPU‐based execution. Furthermore, results indicate that the IFM size and transfer latency are reduced by 34.0%–85.2% and 4.4%–75.7%, respectively, compared to the case without data compression. In addition, the authors' hardware accelerator shows better performance per hardware resource with less than or comparable power consumption to the state‐of‐the‐art FPGA‐based designs.https://doi.org/10.1049/cdt2.12038acceleratorcompressionconvolutional neural networksfield programmable gate arrayinput sparsity |
| spellingShingle | Jisu Kwon Joonho Kong Arslan Munir Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systems IET Computers & Digital Techniques accelerator compression convolutional neural networks field programmable gate array input sparsity |
| title | Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systems |
| title_full | Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systems |
| title_fullStr | Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systems |
| title_full_unstemmed | Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systems |
| title_short | Sparse convolutional neural network acceleration with lossless input feature map compression for resource‐constrained systems |
| title_sort | sparse convolutional neural network acceleration with lossless input feature map compression for resource constrained systems |
| topic | accelerator compression convolutional neural networks field programmable gate array input sparsity |
| url | https://doi.org/10.1049/cdt2.12038 |
| work_keys_str_mv | AT jisukwon sparseconvolutionalneuralnetworkaccelerationwithlosslessinputfeaturemapcompressionforresourceconstrainedsystems AT joonhokong sparseconvolutionalneuralnetworkaccelerationwithlosslessinputfeaturemapcompressionforresourceconstrainedsystems AT arslanmunir sparseconvolutionalneuralnetworkaccelerationwithlosslessinputfeaturemapcompressionforresourceconstrainedsystems |