{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:03:42Z","timestamp":1776783822422,"version":"3.51.2"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"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":["Circuits Syst Signal Process"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s00034-024-02920-x","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T20:19:36Z","timestamp":1733257176000},"page":"2527-2561","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Lightweight Low-Power U-Net Architecture for Semantic Segmentation"],"prefix":"10.1007","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4611-2334","authenticated-orcid":false,"given":"Chaitanya","family":"Modiboyina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Indrajit","family":"Chakrabarti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soumya Kanti","family":"Ghosh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"issue":"4","key":"2920_CR1","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1109\/TCSI.2017.2757036","volume":"65","author":"A Ardakani","year":"2018","unstructured":"A. Ardakani, C. Condo, M. Ahmadi, W.J. Gross, An architecture to accelerate convolution in deep neural networks. IEEE Trans. Circuits Syst. I Regul. Pap. 65(4), 1349\u20131362 (2018)","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"issue":"10","key":"2920_CR2","first-page":"1415","volume":"65","author":"L Bai","year":"2018","unstructured":"L. Bai, Y. Zhao, X. Huang, A CNN accelerator on FPGA using depthwise separable convolution. IEEE Trans. Circuits Syst. II Express Briefs 65(10), 1415\u20131419 (2018)","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"2920_CR3","doi-asserted-by":"crossref","unstructured":"A. Bulat, G. Tzimiropoulos, Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources. in 2017 IEEE International Conference on Computer Vision (ICCV), pp.3726\u20133734 (2017).","DOI":"10.1109\/ICCV.2017.400"},{"issue":"1","key":"2920_CR4","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/JSSC.2016.2616357","volume":"52","author":"Y-H Chen","year":"2017","unstructured":"Y.-H. Chen, T. Krishna, J.S. Emer, V. Sze, Eyeriss: an energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE J. Solid-State Circuits 52(1), 127\u2013138 (2017)","journal-title":"IEEE J. Solid-State Circuits"},{"key":"2920_CR5","unstructured":"M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, Y. Bengio, Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. arXiv preprint http:\/\/arxiv.org\/abs\/1602.02830 (2016)."},{"issue":"19","key":"2920_CR6","doi-asserted-by":"publisher","first-page":"3836","DOI":"10.3390\/rs13193836","volume":"13","author":"C Dechesne","year":"2021","unstructured":"C. Dechesne, P. Lassalle, S. Lef\u00e8vre, Bayesian u-net: Estimating uncertainty in semantic segmentation of earth observation images. Remote Sensing. 13(19), 3836 (2021)","journal-title":"Remote Sensing."},{"key":"2920_CR7","doi-asserted-by":"crossref","unstructured":"A. Esmaeilzehi, L. Ma, M. O. Ahmad, Towards Analyzing the Robustness of Deep Light-weight Image Super Resolution Networks under Distribution Shift. in 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), pp. 1\u20136 (2022).","DOI":"10.1109\/MMSP55362.2022.9948963"},{"key":"2920_CR8","doi-asserted-by":"crossref","unstructured":"A. Esmaeilzehi, M. O. Ahmad, M. N. S. Swamy, Srnmfrb: A Deep Light-Weight Super Resolution Network Using Multi-Receptive Field Feature Generation Residual Blocks. in 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2020).","DOI":"10.1109\/ICME46284.2020.9102951"},{"key":"2920_CR9","doi-asserted-by":"crossref","unstructured":"A. Esmaeilzehi, M. O. Ahmad, M. N. S. Swamy, FPNet: A Deep Light-Weight Interpretable Neural Network Using Forward Prediction Filtering for Efficient Single Image Super Resolution. IEEE Trans. Circuits Syst II: Express Briefs, 69(3), 1937\u20131941 (2021).","DOI":"10.1109\/TCSII.2021.3121667"},{"issue":"6","key":"2920_CR10","doi-asserted-by":"publisher","first-page":"1724","DOI":"10.1109\/TAI.2022.3224417","volume":"4","author":"A Esmaeilzehi","year":"2023","unstructured":"A. Esmaeilzehi, M.O. Ahmad, M.N.S. Swamy, Ultralight-Weight Three-Prior Convolutional Neural Network for Single Image Super Resolution. IEEE Trans. Artificial Intelligence 4(6), 1724\u20131738 (2023)","journal-title":"IEEE Trans. Artificial Intelligence"},{"key":"2920_CR11","doi-asserted-by":"crossref","unstructured":"S. Fang, L. Tian, J. Wang, S. Liang, D. Xie, Z. Chen, L. Sui, Q. Yu, X. Sun, Y. Shan, Y. Wang, Real-time object detection and semantic segmentation hardware system with deep learning networks. in 2018 International Conference on Field-Programmable Technology (FPT), pp. 389\u2013392 (2018).","DOI":"10.1109\/FPT.2018.00081"},{"issue":"1","key":"2920_CR12","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1109\/TCAD.2017.2705069","volume":"37","author":"K Guo","year":"2018","unstructured":"K. Guo, L. Sui, J. Qiu, J. Yu, J. Wang, S. Yao, S. Han, Y. Wang, H. Yang, Angel-eye: a complete design flow for mapping cnn onto embedded fpga. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 37(1), 35\u201347 (2018)","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"2920_CR13","unstructured":"S. Han, J. Pool, J. Tran, W.J. Dally, Learning both weights and connections for efficient neural networks. in Proceedings of the 28th International Conference on Neural Information Processing Systems (NeurIPS), pp. 1135\u20131143 (2015)."},{"issue":"14","key":"2920_CR14","doi-asserted-by":"publisher","first-page":"3969","DOI":"10.3390\/s20143969","volume":"20","author":"H Huang","year":"2020","unstructured":"H. Huang, Y. Wu, M. Yu, X. Shi, F. Qiao, L. Luo, Q. Wei, X. Liu, Edssa: An encoder-decoder semantic segmentation networks accelerator on opencl-based fpga platform. Sensors 20(14), 3969 (2020)","journal-title":"Sensors"},{"key":"2920_CR15","doi-asserted-by":"crossref","unstructured":"W. Jia, J. Cui, X. Zheng, Q. Wu, Design and implementation of real-time semantic segmentation network based on fpga. in Proceedings of the 2021 7th International Conference on Computing and Artificial Intelligence (ICAIIC), pp. 321\u2013325 (2021).","DOI":"10.1145\/3467707.3467756"},{"key":"2920_CR16","unstructured":"H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. Peter Graf, Pruning filters for efficient convnets. in International Conference on Learning Representations (ICLR), pp. 1\u201313 (2017)."},{"key":"2920_CR17","doi-asserted-by":"publisher","first-page":"3547","DOI":"10.1007\/s00034-022-01952-5","volume":"41","author":"H-J Lin","year":"2022","unstructured":"H.-J. Lin, C.-A. Shen, The data flow and architectural optimizations for a highly efficient cnn accelerator based on the depthwise separable convolution. Circuits Syst. Signal Process 41, 3547\u20133569 (2022)","journal-title":"Circuits Syst. Signal Process"},{"key":"2920_CR18","doi-asserted-by":"publisher","first-page":"4759","DOI":"10.1007\/s00034-023-02331-4","volume":"42","author":"H-W Liu","year":"2023","unstructured":"H.-W. Liu, C.-A. Shen, The design of efficient data flow and low-complexity architecture for a highly configurable cnn accelerator. Circuits Syst. Signal Process 42, 4759\u20134783 (2023)","journal-title":"Circuits Syst. Signal Process"},{"key":"2920_CR19","doi-asserted-by":"crossref","unstructured":"S. Liu, H. Fan, X. Niu, H.-C. Ng, Y. Chu, W. Luk, Optimizing cnn-based segmentation with deeply customized convolutional and deconvolutional architectures on fpga. ACM Trans. Reconfigurable Technol. Syst. 11(3) (2018).","DOI":"10.1145\/3242900"},{"key":"2920_CR20","doi-asserted-by":"crossref","unstructured":"S. Liu, W. Luk, Towards an efficient accelerator for dnn-based remote sensing image segmentation on fpgas. in 2019 29th International Conference on Field Programmable Logic and Applications (FPL), pp. 187\u2013193 (2019).","DOI":"10.1109\/FPL.2019.00037"},{"key":"2920_CR21","doi-asserted-by":"crossref","unstructured":"N. Ma, X. Zhang, H.-T. Zheng, J. Sun, Shufflenet v2: Practical guidelines for efficient cnn architecture design. in Proceedings of the European Conference on Computer Vision (ECCV), pp. 116\u2013131 (2018).","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"2920_CR22","doi-asserted-by":"crossref","unstructured":"F. Milletari, N. Navab, S.-A. Ahmadi, V-net: Fully convolutional neural networks for volumetric medical image segmentation. in Proceedings of the Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571 (2016).","DOI":"10.1109\/3DV.2016.79"},{"issue":"20","key":"2920_CR23","doi-asserted-by":"publisher","first-page":"17723","DOI":"10.1007\/s00521-022-07419-7","volume":"34","author":"M Mubashir","year":"2022","unstructured":"M. Mubashir, H. Ali, C. Gr\u00f6nlund, S. Azmat, R2u++: A multiscale recurrent residual u-net with dense skip connections for medical image segmentation. Neural Comput. Appl. 34(20), 17723\u201317739 (2022)","journal-title":"Neural Comput. Appl."},{"issue":"8","key":"2920_CR24","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.1109\/TVLSI.2019.2905242","volume":"27","author":"D-T Nguyen","year":"2019","unstructured":"D.-T. Nguyen, T.N. Nguyen, H. Kim, H.-J. Lee, A high-throughput and power efficient fpga implementation of yolo cnn for object detection. IEEE Trans. Very Large Scale Integr. (VLSI) Syst. 27(8), 1861\u20131873 (2019)","journal-title":"IEEE Trans. Very Large Scale Integr. (VLSI) Syst."},{"key":"2920_CR25","unstructured":"D. Przewlocka-Rus, S.S. Sarwa, H. E. Sumbul, Y. Li, B. De Salvo, Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks.\u00a0arXivpreprint https:\/\/arxiv.org\/abs\/2203.05025. (2022)."},{"key":"2920_CR26","doi-asserted-by":"crossref","unstructured":"D. Przewlocka-Rus, T. Kryjak. 2023. Energy efficient hardware acceleration of neural networks with power-of-two quantisation. in Internation Conference on Computer Vision and Graphics (ICCVG). Springer. Cham. 225\u2013236","DOI":"10.1007\/978-3-031-22025-8_16"},{"issue":"1","key":"2920_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3376922","volume":"16","author":"NS Punn","year":"2020","unstructured":"N.S. Punn, S. Agarwal, Inception u-net architecture for semantic segmentation to identify nuclei in microscopy cell images. ACM Trans. Multimedia Comput. Commun. Appl. 16(1), 1\u201315 (2020)","journal-title":"ACM Trans. Multimedia Comput. Commun. Appl."},{"key":"2920_CR28","doi-asserted-by":"crossref","unstructured":"M. Rastegari, V. Ordonez, J. Redmon, A. Farhadi, Xnor-net: Imagenet classification using binary convolutional neural networks. in Proceedings of European Conference on Computer Vision (ECCV), pp. 525\u2013542 (2016).","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"2920_CR29","doi-asserted-by":"publisher","first-page":"6089","DOI":"10.1007\/s00034-023-02387-2","volume":"42","author":"G Raut","year":"2023","unstructured":"G. Raut, J. Mukala, V. Sharma, S.K. Vishvakarma, Designing a performance-centric mac unit with pipelined architecture for dnn accelerators. Circuits Syst. Signal Process 42, 6089\u20136115 (2023)","journal-title":"Circuits Syst. Signal Process"},{"key":"2920_CR30","doi-asserted-by":"crossref","unstructured":"O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation. in Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234\u2013241. Springer, Cham (2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2920_CR31","first-page":"31","volume":"365","author":"L Rundo","year":"2019","unstructured":"L. Rundo, C. Han, Y. Nagano, J. Zhang, R. Hataya, C. Militello, A. Tangherloni, M.S. Nobile, C. Ferretti, D. Besozzi, M.C. Gilardi, S. Vitabile, G. Mauri, H. Nakayama, P. Cazzaniga, Use-net: Incorporating squeeze-and-excitation blocks into u-net for prostate zonal segmentation of multi-institutional mri datasets. Neuro computing 365, 31\u201343 (2019)","journal-title":"Neuro computing"},{"issue":"3","key":"2920_CR32","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1016\/j.bbe.2020.05.006","volume":"40","author":"N Sambyal","year":"2020","unstructured":"N. Sambyal, P. Saini, R. Syal, V. Gupta, Modified u-net architecture for semantic segmentation of diabetic retinopathy images. Biocybern. Biomed. Eng. 40(3), 1094\u20131109 (2020)","journal-title":"Biocybern. Biomed. Eng."},{"key":"2920_CR33","doi-asserted-by":"crossref","unstructured":"M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510\u20134520 (2018).","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2920_CR34","doi-asserted-by":"publisher","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","volume":"9","author":"N Siddique","year":"2021","unstructured":"N. Siddique, S. Paheding, C.P. Elkin, V. Devabhaktuni, U-net and its variants for medical image segmentation: A review of theory and applications. IEEE Access 9, 82031\u201382057 (2021)","journal-title":"IEEE Access"},{"key":"2920_CR35","doi-asserted-by":"crossref","unstructured":"H. Song, Y.Wang, S. Zeng, X. Guo, Z. Li, Oau-net: Outlined attention u-net for biomedical image segmentation. Biomed. Signal Process.Control 79 (2023).","DOI":"10.1016\/j.bspc.2022.104038"},{"key":"2920_CR36","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1007\/s10766-021-00712-3","volume":"49","author":"R Stahl","year":"2021","unstructured":"R. Stahl, A. Hoffman, D. Mueller Gritschneder, A. Gerstlauer, U. Schlichtmann, Deeperthings: fully distributed cnn inference on resourceconstrained edge devices. Int. J. Parallel Program 49, 600\u2013624 (2021)","journal-title":"Int. J. Parallel Program"},{"issue":"6","key":"2920_CR37","doi-asserted-by":"publisher","first-page":"159","DOI":"10.3390\/a14060159","volume":"14","author":"F Sun","year":"2021","unstructured":"F. Sun et al., Circle-u-net: An efficient architecture for semantic segmentation. Algorithms. 14(6), 159 (2021)","journal-title":"Algorithms."},{"key":"2920_CR38","doi-asserted-by":"crossref","unstructured":"R. Szeliski, 2010. Computer Vision: Algorithms and Applications. Springer. Cham. 187\u2013271","DOI":"10.1007\/978-1-84882-935-0"},{"issue":"8","key":"2920_CR39","doi-asserted-by":"publisher","first-page":"2220","DOI":"10.1109\/TVLSI.2017.2688340","volume":"25","author":"F Tu","year":"2017","unstructured":"F. Tu, S. Yin, P. Ouyang, S. Tang, L. Liu, S. Wei, Deep convolutional neural network architecture with reconfigurable computation patterns. IEEE Trans. Very Large Scale Integr. VLSI Syst. 25(8), 2220\u20132233 (2017)","journal-title":"IEEE Trans. Very Large Scale Integr. VLSI Syst."},{"key":"2920_CR40","doi-asserted-by":"crossref","unstructured":"V. Venkata Bhargava Narendra, P. Rangababu, B. K. Balabantaray. 2021. Lowpower u-net for semantic image segmentation. in Machine Learning Deep Learning and Computational Intelligence for Wireless Communication (MDCWC). Springer. Singapore. 473\u2013491","DOI":"10.1007\/978-981-16-0289-4_35"},{"key":"2920_CR41","unstructured":"S. Wu, G. Li, F. Chen, and L. Shi, Training and Inference with Integers in Deep Neural Networks. arXiv preprint http:\/\/arxiv.org\/abs\/1802.04680. (2018)"},{"issue":"1","key":"2920_CR42","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1109\/TVLSI.2019.2939726","volume":"28","author":"Y Yu","year":"2020","unstructured":"Y. Yu, C. Wu, T. Zhao, K. Wang, L. He, Opu: An fpga-based overlay processor for convolutional neural networks. IEEE Trans. Very Large Scale Integr. VLSI Syst. 28(1), 35\u201347 (2020)","journal-title":"IEEE Trans. Very Large Scale Integr. VLSI Syst."},{"key":"2920_CR43","doi-asserted-by":"crossref","unstructured":"Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, J. Liang. (2018). Unet++: A nested u-net architecture for medical image segmentation. in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311. Springer. Cham","DOI":"10.1007\/978-3-030-00889-5_1"}],"container-title":["Circuits, Systems, and Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-024-02920-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00034-024-02920-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00034-024-02920-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T16:20:36Z","timestamp":1742314836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00034-024-02920-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["2920"],"URL":"https:\/\/doi.org\/10.1007\/s00034-024-02920-x","relation":{},"ISSN":["0278-081X","1531-5878"],"issn-type":[{"value":"0278-081X","type":"print"},{"value":"1531-5878","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"6 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 November 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Further, the authors declare that there are no conflicts of interests\/competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests"}}]}}