{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T01:30:15Z","timestamp":1743816615205,"version":"3.37.3"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T00:00:00Z","timestamp":1696982400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T00:00:00Z","timestamp":1696982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U22A20102"],"award-info":[{"award-number":["U22A20102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Ningbo Natural Science Foundation project","award":["No.2023J400"],"award-info":[{"award-number":["No.2023J400"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Real-Time Image Proc"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11554-023-01366-9","type":"journal-article","created":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T12:02:49Z","timestamp":1697025769000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pse: mixed quantization framework of neural networks for efficient deployment"],"prefix":"10.1007","volume":"20","author":[{"given":"Yingqing","family":"Yang","sequence":"first","affiliation":[]},{"given":"Guanzhong","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Mingyuan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yihao","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Longhua","family":"Ma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,11]]},"reference":[{"key":"1366_CR1","doi-asserted-by":"crossref","unstructured":"Bucila, C., Caruana, R., Niculescu-Mizil, A.: Model compression. In: KDD \u201906 (2006)","DOI":"10.1145\/1150402.1150464"},{"key":"1366_CR2","doi-asserted-by":"crossref","unstructured":"Cai, Y., Yao, Z., Dong, Z., Gholami, A., Mahoney, M.W., Keutzer, K.: Zeroq: a novel zero shot quantization framework. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13169\u201313178 (2020)","DOI":"10.1109\/CVPR42600.2020.01318"},{"key":"1366_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109780","volume":"143","author":"J Chen","year":"2023","unstructured":"Chen, J., Bai, S., Huang, T., Wang, M., Tian, G., Liu, Y.: Data-free quantization via mixed-precision compensation without fine-tuning. Pattern Recognit. 143, 109780 (2023)","journal-title":"Pattern Recognit."},{"issue":"3","key":"1366_CR4","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1109\/TNNLS.2020.2980041","volume":"32","author":"J Chen","year":"2021","unstructured":"Chen, J., Liu, L., Liu, Y., Zeng, X.: A learning framework for n-bit quantized neural networks toward fpgas. IEEE Trans. Neural Netw. Learn. Syst. 32(3), 1067\u20131081 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2020.2980041","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1366_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Du, Z., Sun, N., Wang, J., Wu, C., Chen, Y., Temam, O.: Diannao: a small-footprint high-throughput accelerator for ubiquitous machine-learning. in: Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (2014)","DOI":"10.1145\/2541940.2541967"},{"issue":"2","key":"1366_CR6","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MM.2014.12","volume":"34","author":"L Codrescu","year":"2014","unstructured":"Codrescu, L., Anderson, W., Venkumanhanti, S., Zeng, M., Plondke, E., Koob, C., Ingle, A., Tabony, C., Maule, R.: Hexagon dsp: an architecture optimized for mobile multimedia and communications. IEEE Micro 34(2), 34\u201343 (2014). https:\/\/doi.org\/10.1109\/MM.2014.12","journal-title":"IEEE Micro"},{"key":"1366_CR7","unstructured":"Courbariaux, M., Hubara, I., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks: training deep neural networks with weights and activations constrained to + 1 or - 1. arXiv preprint arXiv:1602.02830 (2016)"},{"key":"1366_CR8","unstructured":"Dettmers, T.: 8-bit approximations for parallelism in deep learning. arXiv preprint arXiv:1511.04561 (2015)"},{"key":"1366_CR9","unstructured":"Fan, A., Stock, P., Graham, B., Grave, E., Gribonval, R., Jegou, H., Joulin, A.: Training with quantization noise for extreme model compression. arXiv preprint arXiv:2004.07320 (2020)"},{"key":"1366_CR10","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.neucom.2022.08.002","volume":"507","author":"J Fan","year":"2022","unstructured":"Fan, J., Pan, Z., Wang, L., Wang, Y.: Codebook-softened product quantization for high accuracy approximate nearest neighbor search. Neurocomputing 507, 107\u2013116 (2022)","journal-title":"Neurocomputing"},{"key":"1366_CR11","first-page":"1050","volume-title":"International Conference on machine Learning","author":"Y Gal","year":"2016","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: International Conference on machine Learning, pp. 1050\u20131059. PMLR (2016)"},{"key":"1366_CR12","doi-asserted-by":"crossref","unstructured":"Ge, T., He, K., Ke, Q., Sun, J.: Optimized product quantization for approximate nearest neighbor search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)","DOI":"10.1109\/CVPR.2013.379"},{"key":"1366_CR13","unstructured":"Gong, Y., Liu, L., Yang, M., Bourdev, L.: Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115 (2014)"},{"key":"1366_CR14","first-page":"1737","volume-title":"Rroceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research","author":"S Gupta","year":"2015","unstructured":"Gupta, S., Agrawal, A., Gopalakrishnan, K., Narayanan, P.: Deep learning with limited numerical precision. In: Bach, F., Blei, D. (eds.) Rroceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research, vol. 37, pp. 1737\u20131746. PMLR, Lille (2015)"},{"key":"1366_CR15","doi-asserted-by":"crossref","unstructured":"Han, S., Liu, X., Mao, H., Pu, J., Pedram, A., Horowitz, M., Dally, W.J.: Eie: Efficient inference engine on compressed deep neural network. 2016 ACM\/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA) pp. 243\u2013254 (2016)","DOI":"10.1109\/ISCA.2016.30"},{"key":"1366_CR16","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. Vision and Pattern Recognition. arXiv: Computer (2016)"},{"key":"1366_CR17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1366_CR18","unstructured":"Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. ArXiv abs\/1503.02531 (2015)"},{"issue":"9","key":"1366_CR19","doi-asserted-by":"publisher","first-page":"3618","DOI":"10.1109\/TCSVT.2020.3040367","volume":"31","author":"W Hong","year":"2021","unstructured":"Hong, W., Chen, T., Lu, M., Pu, S., Ma, Z.: Efficient neural image decoding via fixed-point inference. IEEE Trans. Circuits Syst. Video Technol. 31(9), 3618\u20133630 (2021). https:\/\/doi.org\/10.1109\/TCSVT.2020.3040367","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1366_CR20","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"issue":"10","key":"1366_CR21","doi-asserted-by":"publisher","first-page":"7269","DOI":"10.1109\/TCSVT.2022.3178178","volume":"32","author":"B Hu","year":"2022","unstructured":"Hu, B., Zhou, S., Xiong, Z., Wu, F.: Cross-resolution distillation for efficient 3D medical image registration. IEEE Trans. Circuits Syst. Video Technol. 32(10), 7269\u20137283 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2022.3178178","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1366_CR22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"1366_CR23","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)"},{"key":"1366_CR24","doi-asserted-by":"crossref","unstructured":"Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., Kalenichenko, D.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704\u20132713 (2018)","DOI":"10.1109\/CVPR.2018.00286"},{"key":"1366_CR25","unstructured":"Jin, Q., Ren, J., Zhuang, R., Hanumante, S., Li, Z., Chen, Z., Wang, Y., Yang, K., Tulyakov, S.: F8net: Fixed-point 8-bit only multiplication for network quantization. arXiv preprint arXiv:2202.05239 (2022)"},{"issue":"1","key":"1366_CR26","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1109\/TPAMI.2010.57","volume":"33","author":"H J\u00e9gou","year":"2011","unstructured":"J\u00e9gou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117\u2013128 (2011). https:\/\/doi.org\/10.1109\/TPAMI.2010.57","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1366_CR27","unstructured":"Krishnamoorthi, R.: Quantizing deep convolutional networks for efficient inference: a whitepaper. arXiv preprint arXiv:1806.08342 (2018)"},{"key":"1366_CR28","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images. (2009)"},{"key":"1366_CR29","unstructured":"Li, F., Zhang, B., Liu, B.: Ternary weight networks. arXiv preprint arXiv:1605.04711 (2016)"},{"key":"1366_CR30","doi-asserted-by":"publisher","first-page":"1949","DOI":"10.1007\/s11554-020-01053-z","volume":"18","author":"Z Li","year":"2021","unstructured":"Li, Z., Sun, Y., Tian, G., Xie, L., Liu, Y., Su, H., He, Y.: A compression pipeline for one-stage object detection model. J. Real-Time Image Process. 18, 1949\u20131962 (2021)","journal-title":"J. Real-Time Image Process."},{"key":"1366_CR31","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.neucom.2021.07.045","volume":"461","author":"T Liang","year":"2021","unstructured":"Liang, T., Glossner, J., Wang, L., Shi, S., Zhang, X.: Pruning and quantization for deep neural network acceleration: a survey. Neurocomputing 461, 370\u2013403 (2021)","journal-title":"Neurocomputing"},{"key":"1366_CR32","first-page":"11711","volume-title":"Advances in Neural Information Processing Systems","author":"J Lin","year":"2020","unstructured":"Lin, J., Chen, W.M., Lin, Y., Cohn, J., Gan, C., Han, S.: Mcunet: tiny deep learning on IoT devices. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 11711\u201311722. Curran Associates Inc (2020)"},{"issue":"9","key":"1366_CR33","doi-asserted-by":"publisher","first-page":"6414","DOI":"10.1109\/TCSVT.2022.3166803","volume":"32","author":"C Liu","year":"2022","unstructured":"Liu, C., Ding, W., Chen, P., Zhuang, B., Wang, Y., Zhao, Y., Zhang, B., Han, Y.: Rb-net: training highly accurate and efficient binary neural networks with reshaped point-wise convolution and balanced activation. IEEE Trans. Circuits Syst. Video Technol. 32(9), 6414\u20136424 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2022.3166803","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1366_CR34","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.neucom.2023.01.014","volume":"526","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Wu, D., Zhou, W., Fan, K., Zhou, Z.: Eacp: an effective automatic channel pruning for neural networks. Neurocomputing 526, 131\u2013142 (2023)","journal-title":"Neurocomputing"},{"key":"1366_CR35","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.neucom.2023.01.014","volume":"526","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Wu, D., Zhou, W., Fan, K., Zhou, Z.: Eacp: an effective automatic channel pruning for neural networks. Neurocomputing 526, 131\u2013142 (2023). https:\/\/doi.org\/10.1016\/j.neucom.2023.01.014","journal-title":"Neurocomputing"},{"key":"1366_CR36","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wu, B., Luo, W., Yang, X., Liu, W., Cheng, K.: Bi-real net: enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01267-0_44"},{"key":"1366_CR37","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.T., Sun, J.: 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"},{"issue":"6","key":"1366_CR38","doi-asserted-by":"publisher","first-page":"2450","DOI":"10.1109\/TCSVT.2020.3020569","volume":"31","author":"DT Nguyen","year":"2021","unstructured":"Nguyen, D.T., Kim, H., Lee, H.J.: Layer-specific optimization for mixed data flow with mixed precision in FPGA design for CNN-based object detectors. IEEE Trans. Circuits Syst. Video Technol. 31(6), 2450\u20132464 (2021). https:\/\/doi.org\/10.1109\/TCSVT.2020.3020569","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"1366_CR39","first-page":"8026","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural. Inf. Process. Syst. 32, 8026\u20138037 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1366_CR40","doi-asserted-by":"crossref","unstructured":"Patel, G., Mopuri, K.R., Qiu, Q.: Learning to retain while acquiring: combating distribution-shift in adversarial data-free knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7786\u20137794 (2023)","DOI":"10.1109\/CVPR52729.2023.00752"},{"key":"1366_CR41","doi-asserted-by":"crossref","unstructured":"Qiu, J., Wang, J., Yao, S., Guo, K., Li, B., Zhou, E., Yu, J., Tang, T., Xu, N., Song, S., Wang, Y., Yang, H.: Going deeper with embedded fpga platform for convolutional neural network. In: Proceedings of the 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays (2016)","DOI":"10.1145\/2847263.2847265"},{"key":"1366_CR42","first-page":"525","volume-title":"European Conference on Computer Vision","author":"M Rastegari","year":"2016","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: Imagenet classification using binary convolutional neural networks. In: European Conference on Computer Vision, pp. 525\u2013542. Springer (2016)"},{"issue":"3","key":"1366_CR43","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015)","journal-title":"Int. J. Comput. Vision"},{"key":"1366_CR44","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510\u20134520 (2018)","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1366_CR45","doi-asserted-by":"publisher","first-page":"27546","DOI":"10.1109\/ACCESS.2023.3258360","volume":"11","author":"R Sayed","year":"2023","unstructured":"Sayed, R., Azmi, H., Shawkey, H., Khalil, A.H., Refky, M.: A systematic literature review on binary neural networks. IEEE Access 11, 27546\u201327578 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3258360","journal-title":"IEEE Access"},{"key":"1366_CR46","first-page":"586","volume-title":"European Conference on Computer Vision","author":"Y Shang","year":"2022","unstructured":"Shang, Y., Xu, D., Zong, Z., Nie, L., Yan, Y.: Network binarization via contrastive learning. In: European Conference on Computer Vision, pp. 586\u2013602. Springer (2022)"},{"key":"1366_CR47","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"1366_CR48","unstructured":"Stock, P., Joulin, A., Gribonval, R., Graham, B., J\u00e9gou, H.: And the bit goes down: Revisiting the quantization of neural networks. arXiv preprint arXiv:1907.05686 (2019)"},{"key":"1366_CR49","first-page":"6105","volume-title":"International Conference on Machine Learning","author":"M Tan","year":"2019","unstructured":"Tan, M., Le, Q.: Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"1366_CR50","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1109\/LSP.2021.3054315","volume":"28","author":"G Tian","year":"2021","unstructured":"Tian, G., Chen, J., Zeng, X., Liu, Y.: Pruning by training: a novel deep neural network compression framework for image processing. IEEE Signal Process. Lett. 28, 344\u2013348 (2021)","journal-title":"IEEE Signal Process. Lett."},{"key":"1366_CR51","first-page":"379","volume-title":"European conference on computer vision","author":"Z Tu","year":"2022","unstructured":"Tu, Z., Chen, X., Ren, P., Wang, Y.: Adabin: improving binary neural networks with adaptive binary sets. In: European conference on computer vision, pp. 379\u2013395. Springer (2022)"},{"key":"1366_CR52","unstructured":"Vanhoucke, V., Senior, A., Mao, M.Z.: Improving the speed of neural networks on cpus (2011)"},{"key":"1366_CR53","doi-asserted-by":"crossref","unstructured":"Wang, K., Liu, Z., Lin, Y., Lin, J., Han, S.: Haq: Hardware-aware automated quantization with mixed precision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00881"},{"key":"1366_CR54","doi-asserted-by":"crossref","unstructured":"Wang, Z., Xiao, H., Lu, J., Zhou, J.: Generalizable mixed-precision quantization via attribution rank preservation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5291\u20135300 (2021)","DOI":"10.1109\/ICCV48922.2021.00524"},{"key":"1366_CR55","doi-asserted-by":"crossref","unstructured":"Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J.: Quantized convolutional neural networks for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4820\u20134828 (2016)","DOI":"10.1109\/CVPR.2016.521"},{"key":"1366_CR56","doi-asserted-by":"crossref","unstructured":"Xu, Z., Lin, M., Liu, J., Chen, J., Shao, L., Gao, Y., Tian, Y., Ji, R.: Recu: Reviving the dead weights in binary neural networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5198\u20135208 (2021)","DOI":"10.1109\/ICCV48922.2021.00515"},{"key":"1366_CR57","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.neucom.2022.07.051","volume":"507","author":"C Yang","year":"2022","unstructured":"Yang, C., Liu, H.: Channel pruning based on convolutional neural network sensitivity. Neurocomputing 507, 97\u2013106 (2022)","journal-title":"Neurocomputing"},{"key":"1366_CR58","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yang, J., Ye, D., Hua, G.: Lq-nets: Learned quantization for highly accurate and compact deep neural networks. ArXiv abs\/1807.10029 (2018)","DOI":"10.1007\/978-3-030-01237-3_23"},{"key":"1366_CR59","doi-asserted-by":"crossref","unstructured":"Zhang, J., Su, Z., Feng, Y., Lu, X., Pietik\u00e4inen, M., Liu, L.: Dynamic binary neural network by learning channel-wise thresholds. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1885\u20131889. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9747328"},{"key":"1366_CR60","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1366_CR61","unstructured":"Zhou, A., Yao, A., Guo, Y., Xu, L., Chen, Y.: Incremental network quantization: Towards lossless CNNS with low-precision weights. arXiv preprint arXiv:1702.03044 (2017)"},{"key":"1366_CR62","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Shen, C., Tan, M., Liu, L., Reid, I.D.: Towards effective low-bitwidth convolutional neural networks. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp. 7920\u20137928 (2018)","DOI":"10.1109\/CVPR.2018.00826"}],"container-title":["Journal of Real-Time Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-023-01366-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11554-023-01366-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11554-023-01366-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T16:26:07Z","timestamp":1700756767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11554-023-01366-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,11]]},"references-count":62,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["1366"],"URL":"https:\/\/doi.org\/10.1007\/s11554-023-01366-9","relation":{},"ISSN":["1861-8200","1861-8219"],"issn-type":[{"type":"print","value":"1861-8200"},{"type":"electronic","value":"1861-8219"}],"subject":[],"published":{"date-parts":[[2023,10,11]]},"assertion":[{"value":"8 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"113"}}