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Syst."],"published-print":{"date-parts":[[2019,11,30]]},"abstract":"<jats:p>Modern and recent architectures of vision-based Convolutional Neural Networks (CNN) have improved detection and prediction accuracy significantly. However, these algorithms are extremely computationally intensive. To break the power and performance wall of CNN computation, we reformulate the CNN computation into an iterative process, where each iteration processes a sub-sample of input features with smaller network and ingests additional features to improve the prediction accuracy. Each smaller network could either classify based on its input set or feed computed and extracted features to the next network to enhance the accuracy. The proposed approach allows early-termination upon reaching acceptable confidence. Moreover, each iteration provides a contextual awareness that allows an intelligent resource allocation and optimization for the proceeding iterations. In this article, we propose various policies to reduce the computational complexity of CNN through the proposed iterative approach. We illustrate how the proposed policies construct a dynamic architecture suitable for a wide range of applications with varied accuracy requirements, resources, and time-budget, without further need for network re-training. Furthermore, we carry out a visualization of the detected features in each iteration through deconvolution network to gain more insight into the successive traversal of the ICNN.<\/jats:p>","DOI":"10.1145\/3355553","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T13:12:30Z","timestamp":1576501950000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["ICNN"],"prefix":"10.1145","volume":"18","author":[{"given":"Katayoun","family":"Neshatpour","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering Department, George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Houman","family":"Homayoun","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Avesta","family":"Sasan","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, George Mason University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,12,14]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835449.1835522"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2694446"},{"key":"e_1_2_1_3_1","volume-title":"Advances in Neuroergonomics and Cognitive Engineering","author":"Saadati Marjan","unstructured":"Marjan Saadati , Jill Nelson , and Hasan Ayaz . 2020. 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In Proceedings of the International Conference on Machine Learning (ICML\u201915). 1737--1746."},{"key":"e_1_2_1_46_1","volume-title":"Proceedings of the International Conference on Machine Learning (ICML\u201916)","author":"Lin Darryl Dexu","unstructured":"Darryl Dexu Lin , Sachin S. Talathi , and V. Sreekanth Annapureddy . 2016. Fixed point quantization of deep convolutional networks . In Proceedings of the International Conference on Machine Learning (ICML\u201916) . 2849--2858. Darryl Dexu Lin, Sachin S. Talathi, and V. Sreekanth Annapureddy. 2016. Fixed point quantization of deep convolutional networks. In Proceedings of the International Conference on Machine Learning (ICML\u201916). 2849--2858."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001177"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/2541940.2541967"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2750389"},{"key":"e_1_2_1_50_1","volume-title":"Accelerating CNN inference on FPGAs: A survey. arXiv preprint arXiv:1806.01683","author":"Abdelouahab Kamel","year":"2018","unstructured":"Kamel Abdelouahab , Maxime Pelcat , Jocelyn Serot , and Fran\u00e7ois Berry . 2018. Accelerating CNN inference on FPGAs: A survey. arXiv preprint arXiv:1806.01683 ( 2018 ). Kamel Abdelouahab, Maxime Pelcat, Jocelyn Serot, and Fran\u00e7ois Berry. 2018. Accelerating CNN inference on FPGAs: A survey. arXiv preprint arXiv:1806.01683 (2018)."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3229631.3229639"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2934583.2934644"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/FCCM.2018.00055"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/2968456.2968458"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.3850\/9783981537079_0819"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"e_1_2_1_58_1","volume-title":"Understanding the exploding gradient problem. CoRR abs\/1211.5063","author":"Pascanu Razvan","year":"2012","unstructured":"Razvan Pascanu , Tomas Mikolov , and Yoshua Bengio . 2012. Understanding the exploding gradient problem. CoRR abs\/1211.5063 ( 2012 ). Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2012. Understanding the exploding gradient problem. CoRR abs\/1211.5063 (2012)."},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISQED.2019.8697497"},{"key":"e_1_2_1_60_1","unstructured":"C. Sidney Burrus et al. 1997. Introduction to wavelets and wavelet transforms: A primer. (1997).  C. Sidney Burrus et al. 1997. Introduction to wavelets and wavelet transforms: A primer. (1997)."},{"key":"e_1_2_1_61_1","volume-title":"Proceedings of the 22nd ACM International Conference on Multimedia. ACM, 675--678","author":"Yangqing","unstructured":"Yangqing Jia et al. 2014. Caffe: Convolutional architecture for fast feature embedding . In Proceedings of the 22nd ACM International Conference on Multimedia. ACM, 675--678 . Yangqing Jia et al. 2014. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM International Conference on Multimedia. ACM, 675--678."},{"key":"e_1_2_1_62_1","volume-title":"A Wavelet Tour of Signal Processing","author":"Mallat St\u00e9phane","unstructured":"St\u00e9phane Mallat . 1999. A Wavelet Tour of Signal Processing . Academic Press . St\u00e9phane Mallat. 1999. A Wavelet Tour of Signal Processing. Academic Press."},{"key":"e_1_2_1_63_1","volume-title":"Haar wavelet-based approach for image compression and quality assessment of compressed image. arXiv preprint arXiv:1010.4084","author":"Talukder Kamrul Hasan","year":"2010","unstructured":"Kamrul Hasan Talukder and Koichi Harada . 2010. Haar wavelet-based approach for image compression and quality assessment of compressed image. arXiv preprint arXiv:1010.4084 ( 2010 ). Kamrul Hasan Talukder and Koichi Harada. 2010. Haar wavelet-based approach for image compression and quality assessment of compressed image. arXiv preprint arXiv:1010.4084 (2010)."},{"key":"e_1_2_1_64_1","volume-title":"Proceedings of the International Conference on Computer Communication and Informatics. 1--4.","author":"Elamaran V.","unstructured":"V. Elamaran and A. Praveen . 2012. Comparison of DCT and wavelets in image coding . In Proceedings of the International Conference on Computer Communication and Informatics. 1--4. V. Elamaran and A. Praveen. 2012. Comparison of DCT and wavelets in image coding. In Proceedings of the International Conference on Computer Communication and Informatics. 1--4."},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10578-9_23"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126474"},{"key":"e_1_2_1_67_1","volume-title":"Is the deconvolution layer the same as a convolutional layer? arXiv preprint arXiv:1609.07009","author":"Shi Wenzhe","year":"2016","unstructured":"Wenzhe Shi , Jose Caballero , Lucas Theis , Ferenc Huszar , Andrew Aitken , Christian Ledig , and Zehan Wang . 2016. Is the deconvolution layer the same as a convolutional layer? arXiv preprint arXiv:1609.07009 ( 2016 ). Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew Aitken, Christian Ledig, and Zehan Wang. 2016. Is the deconvolution layer the same as a convolutional layer? arXiv preprint arXiv:1609.07009 (2016)."},{"key":"e_1_2_1_68_1","volume-title":"Zeiler and Rob Fergus","author":"Matthew","year":"2014","unstructured":"Matthew D. Zeiler and Rob Fergus . 2014 . Visualizing and understanding convolutional networks. In Proceedings of the European Conference on Computer Vision. Springer , 818--833. Matthew D. Zeiler and Rob Fergus. 2014. Visualizing and understanding convolutional networks. In Proceedings of the European Conference on Computer Vision. Springer, 818--833."},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_2_1_70_1","volume-title":"Proceedings of the IEEE International Conference on Computer Vision. 2758--2766","author":"Dosovitskiy Alexey","unstructured":"Alexey Dosovitskiy , Philipp Fischer , Eddy Ilg , Philip Hausser , Caner Hazirbas , Vladimir Golkov , Patrick van der Smagt, Daniel Cremers, and Thomas Brox. 2015. Flownet: Learning optical flow with convolutional networks . In Proceedings of the IEEE International Conference on Computer Vision. 2758--2766 . Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox. 2015. Flownet: Learning optical flow with convolutional networks. In Proceedings of the IEEE International Conference on Computer Vision. 2758--2766."},{"key":"e_1_2_1_71_1","volume-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434","author":"Radford Alec","year":"2015","unstructured":"Alec Radford , Luke Metz , and Soumith Chintala . 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 ( 2015 ). Alec Radford, Luke Metz, and Soumith Chintala. 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/3005348"},{"key":"e_1_2_1_73_1","volume-title":"Dally","author":"Han Song","year":"2015","unstructured":"Song Han , Huizi Mao , and William J . Dally . 2015 . Deep compression: Compressing de ep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015). Song Han, Huizi Mao, and William J. Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)."},{"key":"e_1_2_1_74_1","volume-title":"Dally","author":"Han Song","year":"2016","unstructured":"Song Han , Xingyu Liu , Huizi Mao , Jing Pu , Ardavan Pedram , Mark A. Horowitz , and William J . Dally . 2016 . EIE : Efficient inference engine on compressed deep neural network. In Proceedings of the 43rd International Symposium on Computer Architecture. IEEE Press , 243--254. Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and William J. Dally. 2016. EIE: Efficient inference engine on compressed deep neural network. In Proceedings of the 43rd International Symposium on Computer Architecture. IEEE Press, 243--254."},{"key":"e_1_2_1_75_1","volume-title":"Dally","author":"Han Song","year":"2015","unstructured":"Song Han , Huizi Mao , and William J . Dally . 2015 . Deep compression: Compressing de ep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015). Song Han, Huizi Mao, and William J. Dally. 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149 (2015)."},{"key":"e_1_2_1_76_1","volume-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861","author":"Howard Andrew G.","year":"2017","unstructured":"Andrew G. Howard , Menglong Zhu , Mark Sandler , Andrew Howard , Menglong Zhu , Andrey Zhmoginov , and Liang-Chieh Chen . 2017 . Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017). Andrew G. Howard, Menglong Zhu, Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"}],"container-title":["ACM Transactions on Embedded Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3355553","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3355553","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3355553","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:23:34Z","timestamp":1750202614000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3355553"}},"subtitle":["The Iterative Convolutional Neural Network"],"short-title":[],"issued":{"date-parts":[[2019,11,30]]},"references-count":77,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2019,11,30]]}},"alternative-id":["10.1145\/3355553"],"URL":"https:\/\/doi.org\/10.1145\/3355553","relation":{},"ISSN":["1539-9087","1558-3465"],"issn-type":[{"value":"1539-9087","type":"print"},{"value":"1558-3465","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,30]]},"assertion":[{"value":"2019-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-08-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-12-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}