{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T21:46:47Z","timestamp":1757540807597,"version":"3.37.3"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T00:00:00Z","timestamp":1699660800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T00:00:00Z","timestamp":1699660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Sign Process Syst"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s11265-023-01901-8","type":"journal-article","created":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T06:01:30Z","timestamp":1699682490000},"page":"15-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Accelerating a Meta Learning Model for Ultrasonic Non-Destructive Testing Applications Using Model Compression and FPGA Hardware"],"prefix":"10.1007","volume":"96","author":[{"given":"Yu","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kushal","family":"Virupakshappa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2376-8325","authenticated-orcid":false,"given":"Erdal","family":"Oruklu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,11]]},"reference":[{"key":"1901_CR1","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proceedings of the IEEE"},{"key":"1901_CR2","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097\u20131105.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"1901_CR3","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.\u00a0N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. https:\/\/arxiv.org\/pdf\/1706.03762.pdf"},{"key":"1901_CR4","doi-asserted-by":"publisher","first-page":"1854","DOI":"10.1109\/TUFFC.2020.3045847","volume":"68","author":"RJ Pyle","year":"2021","unstructured":"Pyle, R. J., Bevan, R. L. T., Hughes, R. R., Rachev, R. K., Ali, A. A. S., & Wilcox, P. D. (2021). Deep learning for ultrasonic crack characterization in NDE. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 68, 1854\u20131865. https:\/\/doi.org\/10.1109\/TUFFC.2020.3045847","journal-title":"IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control"},{"key":"1901_CR5","doi-asserted-by":"crossref","unstructured":"Cantero-Chinchilla, S., Wilcox, P.\u00a0D., & Croxford, A.\u00a0J. (2022). Deep learning in automated ultrasonic NDE\u2013developments, axioms and opportunities. NDT & E International, 102703.","DOI":"10.1016\/j.ndteint.2022.102703"},{"key":"1901_CR6","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.neucom.2016.11.066","volume":"257","author":"M Meng","year":"2017","unstructured":"Meng, M., Chua, Y. J., Wouterson, E., & Ong, C. P. K. (2017). Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks. Neurocomputing, 257, 128\u2013135.","journal-title":"Neurocomputing"},{"key":"1901_CR7","doi-asserted-by":"publisher","first-page":"3820","DOI":"10.3390\/s18113820","volume":"18","author":"J Ye","year":"2018","unstructured":"Ye, J., Ito, S., & Toyama, N. (2018). Computerized ultrasonic imaging inspection: from shallow to deep learning. Sensors, 18, 3820.","journal-title":"Sensors"},{"key":"1901_CR8","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.jmapro.2020.01.047","volume":"52","author":"N Amiri","year":"2020","unstructured":"Amiri, N., Farrahi, G., Kashyzadeh, K. R., & Chizari, M. (2020). Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints. Journal of Manufacturing Processes, 52, 26\u201334.","journal-title":"Journal of Manufacturing Processes"},{"key":"1901_CR9","doi-asserted-by":"publisher","unstructured":"Virupakshappa, K., Marino, M., & Oruklu, E. (2018). A multi-resolution convolutional neural network architecture for ultrasonic flaw detection. In: 2018 IEEE International Ultrasonics Symposium (IUS) (pp. 1\u20134). https:\/\/doi.org\/10.1109\/ULTSYM.2018.8579888","DOI":"10.1109\/ULTSYM.2018.8579888"},{"key":"1901_CR10","doi-asserted-by":"crossref","unstructured":"Farabet, C., Poulet, C., Han, J.\u00a0Y., & LeCun, Y. (2009). CNP: An FPGA-based processor for convolutional networks. In 2009 International Conference on Field Programmable Logic and Applications (pp. 32\u201337).","DOI":"10.1109\/FPL.2009.5272559"},{"key":"1901_CR11","unstructured":"Lisp Universal SHell. (2002). http:\/\/lush.sourceforge.net\/"},{"key":"1901_CR12","doi-asserted-by":"publisher","unstructured":"Lian, X., Liu, Z., Song, Z., Dai, J., Zhou, W., & Ji, X. (2019). High-performance FPGA-based CNN accelerator with block-floating-point arithmetic. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 27, 1874\u20131885. https:\/\/doi.org\/10.1109\/TVLSI.2019.2913958","DOI":"10.1109\/TVLSI.2019.2913958"},{"key":"1901_CR13","doi-asserted-by":"crossref","unstructured":"Moss, D.\u00a0J., Krishnan, S., Nurvitadhi, E., Ratuszniak, P., Johnson, C., Sim, J., Mishra, A., Marr, D., Subhaschandra, S., & Leong, P.\u00a0H. (2018). A customizable matrix multiplication framework for the Intel HARPv2 Xeon+ FPGA platform: A deep learning case study. In Proceedings of the 2018 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays (pp. 107\u2013116).","DOI":"10.1145\/3174243.3174258"},{"key":"1901_CR14","doi-asserted-by":"crossref","unstructured":"Kala, S., Jose, B.\u00a0R., Mathew, J., & Nalesh, S. (2019). High-performance CNN Accelerator on FPGA Using Unified Winograd-GEMM Architecture. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 27, 2816\u20132828.","DOI":"10.1109\/TVLSI.2019.2941250"},{"key":"1901_CR15","doi-asserted-by":"crossref","unstructured":"AlBdairi, A.\u00a0J.\u00a0A., Xiao, Z., Alkhayyat, A., Humaidi, A.\u00a0J., Fadhel, M.\u00a0A., Taher, B.\u00a0H., Alzubaidi, L., Santamar\u00eda, J., & Al-Shamma, O. (2022). Face recognition based on deep learning and FPGA for ethnicity identification. Applied Sciences, 12, 2605.","DOI":"10.3390\/app12052605"},{"key":"1901_CR16","doi-asserted-by":"crossref","unstructured":"He, D., He, J., Liu, J., Yang, J., Yan, Q., & Yang, Y. (2021). An FPGA-based LSTM acceleration engine for deep learning frameworks. Electronics, 10, 681.","DOI":"10.3390\/electronics10060681"},{"key":"1901_CR17","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1007\/s11265-022-01756-5","volume":"94","author":"Y Yuan","year":"2022","unstructured":"Yuan, Y., Virupakshappa, K., & Oruklu, E. (2022). FPGA implementation of an ultrasonic flaw detection algorithm based on convolutional neural networks. Journal of Signal Processing Systems, 94, 1447\u20131457.","journal-title":"Journal of Signal Processing Systems"},{"key":"1901_CR18","doi-asserted-by":"publisher","unstructured":"Virupakshappa, K., & Oruklu, E. (2021). Localization of ultrasonic flaws using grid based deep learning. In 2021 IEEE International Ultrasonics Symposium (IUS) (pp. 1\u20133). https:\/\/doi.org\/10.1109\/IUS52206.2021.9593753","DOI":"10.1109\/IUS52206.2021.9593753"},{"key":"1901_CR19","unstructured":"OnScale Solve. (2023). Retrieved March 3, 2022, from https:\/\/onscale.com"},{"key":"1901_CR20","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1121\/1.5021245","volume":"143","author":"BE Treeby","year":"2018","unstructured":"Treeby, B. E., Budisky, J., Wise, E. S., Jaros, J., & Cox, B. T. (2018). Rapid calculation of acoustic fields from arbitrary continuous-wave sources. Journal of the Acoustical Society of America, 143, 529\u2013537.","journal-title":"Journal of the Acoustical Society of America"},{"key":"1901_CR21","doi-asserted-by":"crossref","unstructured":"Virupakshappa, K., & Oruklu, E. (2019). Multi-class classification of defect types in ultrasonic NDT signals with convolutional neural networks. In 2019 IEEE International Ultrasonics Symposium (IUS) (pp. 1647\u20131650).","DOI":"10.1109\/ULTSYM.2019.8926027"},{"key":"1901_CR22","unstructured":"Koch, G., Zemel, R., Salakhutdinov, R., et\u00a0al. (2015). Siamese neural networks for one-shot image recognition. In ICML Deep Learning Workshop (Vol.\u00a02, p.\u00a00). Lille."},{"key":"1901_CR23","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.\u00a0H.\u00a0S., & Hospedales, T.\u00a0M. (2017). Learning to compare: Relation network for few-shot learning. CoRR abs\/1711.06025. http:\/\/arxiv.org\/abs\/1711.06025"},{"key":"1901_CR24","doi-asserted-by":"crossref","unstructured":"Chicco, D. (2021). Siamese neural networks: An overview. Artificial Neural Networks, 73\u201394.","DOI":"10.1007\/978-1-0716-0826-5_3"},{"key":"1901_CR25","doi-asserted-by":"publisher","first-page":"747","DOI":"10.21105\/joss.00747","volume":"4","author":"A LeNail","year":"2019","unstructured":"LeNail, A. (2019). NN-SVG: Publication-ready neural network architecture schematics. Journal of Open Source Software, 4, 747.","journal-title":"Journal of Open Source Software"},{"key":"1901_CR26","doi-asserted-by":"publisher","unstructured":"Zhang, M., Li, L., Wang, H., Liu, Y., Qin, H., & Zhao, W. (2019). Optimized compression for implementing convolutional neural networks on FPGA. Electronics, 8. https:\/\/doi.org\/10.3390\/electronics8030295. https:\/\/www.mdpi.com\/2079-9292\/8\/3\/295","DOI":"10.3390\/electronics8030295"},{"key":"1901_CR27","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Zhang, Y., Wang, Y., & Tian, Q. (2019). Accelerate CNN via recursive bayesian pruning. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV).","DOI":"10.1109\/ICCV.2019.00340"},{"key":"1901_CR28","doi-asserted-by":"publisher","unstructured":"Huang, Q., Zhou, K., You, S., & Neumann, U. (2018). Learning to prune filters in convolutional neural networks. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 709\u2013718). https:\/\/doi.org\/10.1109\/WACV.2018.00083","DOI":"10.1109\/WACV.2018.00083"},{"key":"1901_CR29","unstructured":"Han, S., Pool, J., Tran, J., & Dally, W. (2015). Learning both weights and connections for efficient neural network. Advances in Neural Information Processing Systems, 28."},{"key":"1901_CR30","unstructured":"Gupta, S., Agrawal, A., Gopalakrishnan, K., & Narayanan, P. (2015). Deep learning with limited numerical precision. In International Conference On Machine Learning (pp. 1737\u20131746). PMLR."},{"key":"1901_CR31","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1109\/TCAD.2017.2705069","volume":"37","author":"K Guo","year":"2018","unstructured":"Guo, K., Sui, L., Qiu, J., Yu, J., Wang, J., Yao, S., Han, S., Wang, Y., & Yang, H. (2018). Angel-eye: A complete design flow for mapping CNN onto embedded FPGA. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37, 35\u201347. https:\/\/doi.org\/10.1109\/TCAD.2017.2705069","journal-title":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"},{"key":"1901_CR32","doi-asserted-by":"publisher","unstructured":"Yuan, Y., Virupakshappa, K., & Oruklu, E. (2022). Model compression and FPGA implementation of an ultrasonic flaw detection algorithm based on meta learning. In 2022 IEEE International Ultrasonics Symposium (IUS) (pp. 1\u20134). https:\/\/doi.org\/10.1109\/IUS54386.2022.9958305","DOI":"10.1109\/IUS54386.2022.9958305"},{"key":"1901_CR33","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.\u00a0S., Davis,\u00a0A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia,\u00a0Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Man\u00e9, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke,\u00a0V., Vasudevan, V., Vi\u00e9gas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., & Zheng,\u00a0X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. https:\/\/www.tensorflow.org\/, software available from tensorflow.org."},{"key":"1901_CR34","doi-asserted-by":"publisher","unstructured":"Huang, S., Pearson, C., Nagi, R., Xiong, J., Chen, D., & Hwu, W.-M. (2019). Accelerating sparse deep neural networks on FPGAs. In 2019 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1\u20137). https:\/\/doi.org\/10.1109\/HPEC.2019.8916419","DOI":"10.1109\/HPEC.2019.8916419"},{"key":"1901_CR35","doi-asserted-by":"publisher","unstructured":"Peng, H., Huang, S., Geng, T., Li, A., Jiang, W., Liu, H., Wang, S., & Ding,\u00a0C. (2021). Accelerating transformer-based deep learning models on FPGAs using column balanced block pruning. In 2021 22nd International Symposium on Quality Electronic Design (ISQED)\u00a0(pp. 142\u2013148).\u00a0https:\/\/doi.org\/10.1109\/ISQED51717.2021.9424344","DOI":"10.1109\/ISQED51717.2021.9424344"},{"key":"1901_CR36","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.\u00a0N., Kaiser, \u0141., & Polosukhin,\u00a0I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30."},{"key":"1901_CR37","unstructured":"Alveo U200 Data Center Accelerator Card. (2018).\u00a0Retrieved March 7, 2022, from https:\/\/www.xilinx.com\/products\/boards-and-kits\/alveo\/u200.html"},{"key":"1901_CR38","unstructured":"Intel\u00aeCore\u2122i5-5257U Processor. (2015). Retrieved March 7, 2022, from https:\/\/ark.intel.com\/content\/www\/us\/en\/ark\/products\/84985\/intel-core-i55257u-processor-3m-cache-up-to-3-10-ghz.html"},{"key":"1901_CR39","doi-asserted-by":"publisher","unstructured":"Lu, L., Xie, J., Huang, R., Zhang, J., Lin, W., & Liang, Y. (2019). An efficient hardware accelerator for sparse convolutional neural networks on FPGAs. In 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (pp. 17\u201325). https:\/\/doi.org\/10.1109\/FCCM.2019.00013","DOI":"10.1109\/FCCM.2019.00013"},{"key":"1901_CR40","unstructured":"Deep learning with int8 optimization on Xilinx devices white paper (wp485). (2017). Retrieved October 11, 2022, from https:\/\/www.xilinx.com\/support\/documentation\/whitepapers\/wp486-deep-learning-int8.pdf"},{"key":"1901_CR41","unstructured":"Zedboard. (2020). Retrieved June 14, 2020, from http:\/\/zedboard.org\/product\/zedboard"},{"key":"1901_CR42","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","volume":"17","author":"P Virtanen","year":"2020","unstructured":"Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., \u2026 van Mulbregt, P. (2020). SciPy 1.0 Contributors, SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 17, 261\u2013272. https:\/\/doi.org\/10.1038\/s41592-019-0686-2","journal-title":"Nature Methods"},{"key":"1901_CR43","doi-asserted-by":"crossref","unstructured":"Li, S., Wen, W., Wang, Y., Han, S., Chen, Y., & Li, H. (2017). An FPGA design framework for CNN sparsification and acceleration. In 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (pp. 28\u201328). IEEE.","DOI":"10.1109\/FCCM.2017.21"},{"key":"1901_CR44","doi-asserted-by":"crossref","unstructured":"Guan, Y., Liang, H., Xu, N., Wang, W., Shi, S., Chen, X., Sun, G., Zhang, W., & Cong, J. (2017). FP-DNN: An automated framework for mapping deep neural networks onto FPGAs with RTL-HLS hybrid templates. In 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (pp. 152\u2013159). IEEE.","DOI":"10.1109\/FCCM.2017.25"},{"key":"1901_CR45","doi-asserted-by":"publisher","unstructured":"Jiang, C., Ojika, D., Patel, B., & Lam, H. (2021). Optimized FPGA-based deep learning accelerator for sparse CNN using high bandwidth memory. In 2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) (pp. 157\u2013164). https:\/\/doi.org\/10.1109\/FCCM51124.2021.00026","DOI":"10.1109\/FCCM51124.2021.00026"}],"container-title":["Journal of Signal Processing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-023-01901-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11265-023-01901-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11265-023-01901-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T13:02:47Z","timestamp":1708606967000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11265-023-01901-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,11]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["1901"],"URL":"https:\/\/doi.org\/10.1007\/s11265-023-01901-8","relation":{},"ISSN":["1939-8018","1939-8115"],"issn-type":[{"type":"print","value":"1939-8018"},{"type":"electronic","value":"1939-8115"}],"subject":[],"published":{"date-parts":[[2023,11,11]]},"assertion":[{"value":"13 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study did not include any research involving human participants and\/or animals. Authors have no financial or proprietary interests in any material discussed in this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}