{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T19:45:37Z","timestamp":1771962337392,"version":"3.50.1"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,3,19]],"date-time":"2023-03-19T00:00:00Z","timestamp":1679184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Des. Autom. Electron. Syst."],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>Neural Network (NN)-based real-time inferencing tasks are often co-scheduled on GPGPU-style edge platforms. Existing works advocate using different NN parameters for the same detection task in different environments. However, realizing such approaches remains challenging, given accelerator devices\u2019 limited on-chip memory capacity. As a solution, we propose a multi-pass, time- and space-aware scheduling infrastructure for embedded platforms with GPU accelerators. The framework manages the residency of NN parameters in the limited on-chip memory while simultaneously dispatching relevant compute operations. The mapping decisions for memory operations and compute operations to the underlying resources of the platform are first determined in an offline manner. For this, we proposed a constraint solver-assisted scheduler that optimizes for schedule makespan. This is followed by memory optimization passes, which take the memory budget into account and accordingly adjust the start times of memory and compute operations. Our approach reports a 74%\u201390% savings in peak memory utilization with 0%\u201333% deadline misses for schedules that suffer miss percentage in ranges of 25%\u2013100% when run using existing methods.<\/jats:p>","DOI":"10.1145\/3576197","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T14:45:17Z","timestamp":1671115517000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Inferencing on Edge Devices: A Time- and Space-aware Co-scheduling Approach"],"prefix":"10.1145","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4401-7862","authenticated-orcid":false,"given":"Danny","family":"Pereira","sequence":"first","affiliation":[{"name":"Indian Institute of Technology, Kharagpur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1108-4572","authenticated-orcid":false,"given":"Anirban","family":"Ghose","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Kharagpur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5999-3313","authenticated-orcid":false,"given":"Sumana","family":"Ghosh","sequence":"additional","affiliation":[{"name":"Indian Statistical Institute, Kolkata, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9329-6389","authenticated-orcid":false,"given":"Soumyajit","family":"Dey","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology, Kharagpur, India"}]}],"member":"320","published-online":{"date-parts":[[2023,3,19]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"104","volume-title":"IEEE Real-Time Systems Symposium (RTSS\u201917)","author":"Amert Tanya","year":"2017","unstructured":"Tanya Amert, Nathan Otterness, Ming Yang, James H. Anderson, and F. Donelson Smith. 2017. GPU scheduling on the NVIDIA TX2: Hidden details revealed. In IEEE Real-Time Systems Symposium (RTSS\u201917). IEEE, 104\u2013115."},{"key":"e_1_3_1_3_2","first-page":"305","volume-title":"Satisfiability Modulo Theories","author":"Barrett Clark","year":"2018","unstructured":"Clark Barrett and Cesare Tinelli. 2018. Satisfiability Modulo Theories. Springer International Publishing, Cham, 305\u2013343."},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11241-013-9184-2"},{"key":"e_1_3_1_5_2","first-page":"310","volume-title":"Real-Time and Embedded Technology and Applications Symposium (RTAS\u201920)","author":"Bateni Soroush","year":"2020","unstructured":"Soroush Bateni, Zhendong Wang, Yuankun Zhu, Yang Hu, and Cong Liu. 2020. Co-optimizing performance and memory footprint via integrated CPU\/GPU memory management, an implementation on autonomous driving platform. In Real-Time and Embedded Technology and Applications Symposium (RTAS\u201920). IEEE, 310\u2013323."},{"key":"e_1_3_1_6_2","first-page":"119","volume-title":"IEEE Real-Time Systems Symposium (RTSS\u201918)","author":"Capodieci Nicola","year":"2018","unstructured":"Nicola Capodieci, Roberto Cavicchioli, Marko Bertogna, and Aingara Paramakuru. 2018. Deadline-based scheduling for GPU with preemption support. In IEEE Real-Time Systems Symposium (RTSS\u201918). IEEE, 119\u2013130."},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/MDAT.2016.2573598"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1145\/2809695.2809711","volume-title":"13th ACM Conference on Embedded Networked Sensor Systems","author":"Chen Tiffany Yu-Han","year":"2015","unstructured":"Tiffany Yu-Han Chen, Lenin Ravindranath, Shuo Deng, Paramvir Bahl, and Hari Balakrishnan. 2015. Glimpse: Continuous, real-time object recognition on mobile devices. In 13th ACM Conference on Embedded Networked Sensor Systems. ACM, 155\u2013168."},{"issue":"3","key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1109\/TPDS.2018.2866582","article-title":"moDNN: Memory optimal deep neural network training on graphics processing units","volume":"30","author":"Chen Xiaoming","year":"2018","unstructured":"Xiaoming Chen, Danny Ziyi Chen, Yinhe Han, and Xiaobo Sharon Hu. 2018. moDNN: Memory optimal deep neural network training on graphics processing units. IEEE Trans. Parallel Distrib. Syst. 30, 3 (2018), 646\u2013661.","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"e_1_3_1_10_2","unstructured":"Intel corporation. 2020. OpenVINO. Retrieved from https:\/\/docs.openvinotoolkit.org\/."},{"key":"e_1_3_1_11_2","first-page":"372","volume-title":"12th International Conference on Cloud Computing (CLOUD\u201919)","author":"Dakkak Abdul","year":"2019","unstructured":"Abdul Dakkak, Cheng Li, Simon Garcia De Gonzalo, Jinjun Xiong, and Wen-mei Hwu. 2019. TRIMS: Transparent and isolated model sharing for low latency deep learning inference in function-as-a-service. In 12th International Conference on Cloud Computing (CLOUD\u201919). IEEE, 372\u2013382."},{"key":"e_1_3_1_12_2","first-page":"1","volume-title":"Conference on Information and Communication Technology","author":"Dasgupta Madhuchhanda","year":"2019","unstructured":"Madhuchhanda Dasgupta, Oishila Bandyopadhyay, and Sanjay Chatterji. 2019. Automated helmet detection for multiple motorcycle riders using CNN. In Conference on Information and Communication Technology. IEEE, 1\u20134."},{"key":"e_1_3_1_13_2","volume-title":"Programming Massively Parallel Processors (3rd ed.)","author":"Kirk Wen-mei W. Hwu and David B.","year":"2016","unstructured":"Wen-mei W. Hwu and David B. Kirk. 2016. Programming Massively Parallel Processors (3rd ed.). Morgan Kaufmann."},{"key":"e_1_3_1_14_2","first-page":"389","volume-title":"International Conference on Field-Programmable Technology (FPT\u201918)","author":"Fang Shaoxia","year":"2018","unstructured":"Shaoxia Fang, Lu Tian, Junbin Wang, Shuang Liang, Dongliang Xie, Zhongmin Chen, Lingzhi Sui, Qian Yu, Xiaoming Sun, Yi Shan, and Yu Wang. 2018. Real-time object detection and semantic segmentation hardware system with deep learning networks. In International Conference on Field-Programmable Technology (FPT\u201918). IEEE, 389\u2013392."},{"key":"e_1_3_1_15_2","first-page":"1757","volume-title":"Design, Automation & Test in Europe Conference & Exhibition (DATE\u201921)","author":"Ghose Anirban","year":"2021","unstructured":"Anirban Ghose, Srijeeta Maity, Arijit Kar, and Soumyajit Dey. 2021. Orchestration of perception systems for reliable performance in heterogeneous platforms. In Design, Automation & Test in Europe Conference & Exhibition (DATE\u201921). IEEE, 1757\u20131762."},{"key":"e_1_3_1_16_2","first-page":"43","volume-title":"International Symposium on Performance Analysis of Systems and Software (ISPASS\u201917)","author":"G\u00f3mez-Luna Juan","year":"2017","unstructured":"Juan G\u00f3mez-Luna, Izzat El Hajj, Li-Wen Chang, Victor Garc\u00eda-Floreszx, Simon Garcia De Gonzalo, Thomas B. Jablin, Antonio J. Pena, and Wen-mei Hwu. 2017. Chai: Collaborative heterogeneous applications for integrated-architectures. In International Symposium on Performance Analysis of Systems and Software (ISPASS\u201917). IEEE, 43\u201354."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2021.02.031"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1704.04861"},{"key":"e_1_3_1_19_2","first-page":"2503","volume-title":"IEEE International Conference on Big Data (Big Data\u201918)","author":"Huang Rachel","year":"2018","unstructured":"Rachel Huang, Jonathan Pedoeem, and Cuixian Chen. 2018. YOLO-LITE: A real-time object detection algorithm optimized for non-GPU computers. In IEEE International Conference on Big Data (Big Data\u201918). IEEE, 2503\u20132510."},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3081333.3081360"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1602.07360"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2021.102183"},{"key":"e_1_3_1_23_2","first-page":"253","volume-title":"Conference of the ACM Special Interest Group on Data Communication","author":"Jiang Junchen","year":"2018","unstructured":"Junchen Jiang, Ganesh Ananthanarayanan, Peter Bodik, Siddhartha Sen, and Ion Stoica. 2018. Chameleon: Scalable adaptation of video analytics. In Conference of the ACM Special Interest Group on Data Communication. ACM, 253\u2013266."},{"key":"e_1_3_1_24_2","volume-title":"Efficient Deep Learning Inference on Edge Devices.ACM SysML, Stanford, CA","author":"Jiang Ziheng","year":"2018","unstructured":"Ziheng Jiang, Tianqi Chen, and Mu Li. 2018. Efficient Deep Learning Inference on Edge Devices.ACM SysML, Stanford, CA."},{"key":"e_1_3_1_25_2","first-page":"329","volume-title":"Real-Time Systems Symposium (RTSS\u201921)","author":"Kang Woosung","year":"2021","unstructured":"Woosung Kang, Kilho Lee, Jinkyu Lee, Insik Shin, and Hoon Sung Chwa. 2021. LaLaRAND: Flexible layer-by-layer CPU\/GPU scheduling for real-time DNN tasks. In Real-Time Systems Symposium (RTSS\u201921). IEEE, 329\u2013341."},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1608.08021"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"e_1_3_1_28_2","first-page":"633","volume-title":"International Conference for High Performance Computing, Networking, Storage and Analysis","author":"Li Chao","year":"2016","unstructured":"Chao Li, Yi Yang, Min Feng, Srimat Chakradhar, and Huiyang Zhou. 2016. Optimizing memory efficiency for deep convolutional neural networks on GPUs. In International Conference for High Performance Computing, Networking, Storage and Analysis. ACM, 633\u2013644."},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/MILCOM52596.2021.9652907"},{"key":"e_1_3_1_30_2","first-page":"125","volume-title":"33rd International Conference on VLSI Design and 19th International Conference on Embedded Systems (VLSID\u201920)","author":"Maity Srijeeta","year":"2020","unstructured":"Srijeeta Maity, Anirban Ghose, Soumyajit Dey, and Swarnendu Biswas. 2020. Thermal load-aware adaptive scheduling for heterogeneous platforms. In 33rd International Conference on VLSI Design and 19th International Conference on Embedded Systems (VLSID\u201920). IEEE, 125\u2013130."},{"issue":"5","key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3477028","article-title":"Thermal-aware adaptive platform management for heterogeneous embedded systems","volume":"20","author":"Maity Srijeeta","year":"2021","unstructured":"Srijeeta Maity, Anirban Ghose, Soumyajit Dey, and Swarnendu Biswas. 2021. Thermal-aware adaptive platform management for heterogeneous embedded systems. ACM Trans. Embed. Comput. Syst. 20, 5s (2021), 1\u201328.","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"e_1_3_1_32_2","first-page":"613","volume-title":"International Symposium on High Performance Computer Architecture (HPCA\u201917)","author":"Majumdar Abhinandan","year":"2017","unstructured":"Abhinandan Majumdar, Leonardo Piga, Indrani Paul, Joseph L. Greathouse, Wei Huang, and David H. Albonesi. 2017. Dynamic GPGPU power management using adaptive model predictive control. In International Symposium on High Performance Computer Architecture (HPCA\u201917). IEEE, 613\u2013624."},{"key":"e_1_3_1_33_2","volume-title":"Scheduling Algorithms for Real-time Systems.Technical Report. School of Computing Queens University. Citeseer","author":"Mohammadi Arezou","year":"2005","unstructured":"Arezou Mohammadi and Selim G. Akl. 2005. Scheduling Algorithms for Real-time Systems.Technical Report. School of Computing Queens University. Citeseer."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.5555\/1792734.1792766"},{"key":"e_1_3_1_35_2","unstructured":"Nvidia. 2020. CUDA C++ Programming Guide. Retrieved from https:\/\/docs.nvidia.com\/cuda\/cuda-c-programming-guide\/index.html."},{"key":"e_1_3_1_36_2","unstructured":"Nvidia. 2020. NVIDIA Drive Perception. Retrieved from https:\/\/developer.nvidia.com\/drive\/drive-perception."},{"key":"e_1_3_1_37_2","first-page":"213","volume-title":"IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS\u201920)","author":"Olmedo Ignacio Sa\u00f1udo","year":"2020","unstructured":"Ignacio Sa\u00f1udo Olmedo, Nicola Capodieci, Jorge Luis Martinez, Andrea Marongiu, and Marko Bertogna. 2020. Dissecting the CUDA scheduling hierarchy: A performance and predictability perspective. In IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS\u201920). IEEE, 213\u2013225."},{"key":"e_1_3_1_38_2","first-page":"8544","volume-title":"Chinese Control Conference (CCC\u201919)","author":"Pan Qiushi","year":"2019","unstructured":"Qiushi Pan, Yutong Guo, and Zhiliang Wang. 2019. A scene classification algorithm of visual robot based on Tiny YOLO v2. In Chinese Control Conference (CCC\u201919). IEEE, 8544\u20138549."},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2001.03288"},{"key":"e_1_3_1_40_2","first-page":"1","volume-title":"International Conference for High Performance Computing, Networking, Storage and Analysis","author":"Rajbhandari Samyam","year":"2020","unstructured":"Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, and Yuxiong He. 2020. Zero: Memory optimizations toward training trillion parameter models. In International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 1\u201316."},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4020-9436-1_2"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1804.02767"},{"key":"e_1_3_1_45_2","first-page":"1","volume-title":"49th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO\u201916)","author":"Rhu Minsoo","year":"2016","unstructured":"Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, and Stephen W. Keckler. 2016. vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design. In 49th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO\u201916). IEEE\/ACM, 1\u201313."},{"key":"e_1_3_1_46_2","first-page":"969","volume-title":"Soft Computing for Problem Solving","author":"Saranya Karattupalayam Chidambaram","year":"2020","unstructured":"Karattupalayam Chidambaram Saranya, Arunkumar Thangavelu, Ashwin Chidambaram, Sharan Arumugam, and Sushant Govindraj. 2020. Cyclist detection using Tiny YOLO v2. In Soft Computing for Problem Solving. Springer, 969\u2013979."},{"key":"e_1_3_1_47_2","first-page":"490","volume-title":"4th International Conference on Robotic Computing (IRC\u201920)","author":"Seidaliyeva Ulzhalgas","year":"2020","unstructured":"Ulzhalgas Seidaliyeva, Manal Alduraibi, Lyazzat Ilipbayeva, and Akhan Almagambetov. 2020. Detection of loaded and unloaded UAV using deep neural network. In 4th International Conference on Robotic Computing (IRC\u201920). IEEE, 490\u2013494."},{"key":"e_1_3_1_48_2","first-page":"200","volume-title":"International Parallel and Distributed Processing Symposium (IPDPS\u201919)","author":"Shriram S. B.","year":"2019","unstructured":"S. B. Shriram, Anshuj Garg, and Purushottam Kulkarni. 2019. Dynamic memory management for GPU-based training of deep neural networks. In International Parallel and Distributed Processing Symposium (IPDPS\u201919). IEEE, 200\u2013209."},{"key":"e_1_3_1_49_2","unstructured":"Tensorflow. 2020. TensorFlow-lite. Retrieved from https:\/\/www.tensorflow.org\/lite."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.5555\/3433701.3433726"},{"key":"e_1_3_1_51_2","first-page":"41","volume-title":"23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming","author":"Wang Linnan","year":"2018","unstructured":"Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, and Tim Kraska. 2018. SuperNeurons: Dynamic GPU memory management for training deep neural networks. In 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. ACM, 41\u201353."},{"issue":"4","key":"e_1_3_1_52_2","first-page":"1572","article-title":"Enhanced object detection with deep convolutional neural networks for advanced driving assistance","volume":"21","author":"Wei Jian","year":"2019","unstructured":"Jian Wei, Jianhua He, Yi Zhou, Kai Chen, Zuoyin Tang, and Zhiliang Xiong. 2019. Enhanced object detection with deep convolutional neural networks for advanced driving assistance. IEEE Trans. Intell. Transport. Syst. 21, 4 (2019), 1572\u20131583.","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"e_1_3_1_53_2","first-page":"392","volume-title":"IEEE Real-Time Systems Symposium (RTSS\u201919)","author":"Xiang Yecheng","year":"2019","unstructured":"Yecheng Xiang and Hyoseung Kim. 2019. Pipelined data-parallel CPU\/GPU scheduling for multi-DNN real-time inference. In IEEE Real-Time Systems Symposium (RTSS\u201919). IEEE, 392\u2013405."},{"key":"e_1_3_1_54_2","first-page":"305","volume-title":"IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS\u201919)","author":"Yang Ming","year":"2019","unstructured":"Ming Yang, Shige Wang, Joshua Bakita, Thanh Vu, F. Donelson Smith, James H. Anderson, and Jan-Michael Frahm. 2019. Re-thinking CNN frameworks for time-sensitive autonomous-driving applications: Addressing an industrial challenge. In IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS\u201919). IEEE, 305\u2013317."},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052577"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.2003.03256"},{"key":"e_1_3_1_57_2","first-page":"03002","volume-title":"MATEC Web of Conferences","volume":"336","author":"Zheng Yuanyuan","year":"2021","unstructured":"Yuanyuan Zheng and Jun Ge. 2021. Binocular intelligent following robot based on YOLO-LITE. In MATEC Web of Conferences, Vol. 336. EDP Sciences, 03002."},{"key":"e_1_3_1_58_2","first-page":"190","volume-title":"Real-Time and Embedded Technology and Applications Symposium (RTAS\u201918)","author":"Zhou Husheng","year":"2018","unstructured":"Husheng Zhou, Soroush Bateni, and Cong Liu. 2018. S3DNN: Supervised streaming and scheduling for GPU-accelerated real-time DNN workloads. In Real-Time and Embedded Technology and Applications Symposium (RTAS\u201918). IEEE, 190\u2013201."}],"container-title":["ACM Transactions on Design Automation of Electronic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576197","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3576197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:03Z","timestamp":1750183743000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3576197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,19]]},"references-count":57,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,5,31]]}},"alternative-id":["10.1145\/3576197"],"URL":"https:\/\/doi.org\/10.1145\/3576197","relation":{},"ISSN":["1084-4309","1557-7309"],"issn-type":[{"value":"1084-4309","type":"print"},{"value":"1557-7309","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,19]]},"assertion":[{"value":"2022-07-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-12-04","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-03-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}