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An Efficient Unstructured Sparse Convolutional Neural Network Accelerator for Wearable ECG Classification Device.
- Source :
-
IEEE Transactions on Circuits & Systems. Part I: Regular Papers . Nov2022, Vol. 69 Issue 11, p4572-4582. 11p. - Publication Year :
- 2022
-
Abstract
- Convolution neural network (CNN) with pruning techniques has shown remarkable prospects in electrocardiogram (ECG) classification. However, efficiently deploying the existing pruned neural network to wearable devices for ECG classification is a great challenge due to the limited hardware resource and randomly distributed sparse weights. To address this issue, an efficient unstructured sparse CNN accelerator is proposed in this paper. A tile-first dataflow with compressed data storage format is presented to skip zero weight multiplications and increase the computing efficiency during inference of small-scale model with large sparsity. The two-level weight index matching structure in the dataflow exploits shifting operation to select valid data pairs and maintain the fully-pipelined calculation process. A configurable processing element (PE) array with 32-bit instruction control is proposed to increase the flexibility of the accelerator. Verified in FPGA and post-synthesis simulations in SMIC 40nm process, the proposed sparse CNN accelerator consumes $3.93~\mu $ J/classification at 2MHz clock frequency and it achieves an averaged ECG classification accuracy of 98.99%. A computing efficiency of 118.75% is realized which is improved by 48% compared to the dense baseline. In brief, the proposed efficient CNN accelerator is especially suitable for wearable ECG classification device. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15498328
- Volume :
- 69
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems. Part I: Regular Papers
- Publication Type :
- Periodical
- Accession number :
- 160688682
- Full Text :
- https://doi.org/10.1109/TCSI.2022.3194636