{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T15:55:48Z","timestamp":1780674948368,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":17,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,10]],"date-time":"2022-07-10T00:00:00Z","timestamp":1657411200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","award":["NRF-RSSS2016-05"],"award-info":[{"award-number":["NRF-RSSS2016-05"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,10]]},"DOI":"10.1145\/3489517.3530406","type":"proceedings-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T23:19:29Z","timestamp":1661296769000},"page":"1291-1296","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["REACT"],"prefix":"10.1145","author":[{"given":"Mohit","family":"Upadhyay","sequence":"first","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohan","family":"Juneja","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Wang","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"A*STAR, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weng-Fai","family":"Wong","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Shiuan","family":"Peh","sequence":"additional","affiliation":[{"name":"National University of Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep CNN","author":"Chen Y.","year":"2017","unstructured":"Y. Chen et al. 2017. Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep CNN. IEEE JSSC (2017)."},{"key":"e_1_3_2_1_2_1","volume-title":"TrainWare: A Memory Optimized Weight Update Architecture for On-Device CNN Training. In ISLPED '18","unstructured":"Seungkyu Choi et al. 2018. TrainWare: A Memory Optimized Weight Update Architecture for On-Device CNN Training. In ISLPED '18."},{"key":"e_1_3_2_1_3_1","volume-title":"ASSCC","author":"Choi S.","year":"2019","unstructured":"S. Choi et al. 2019. A 47.4μJ\/epoch Trainable Deep CNN Accelerator for In-Situ Personalization on Smart Devices. In ASSCC 2019. 57--60."},{"key":"e_1_3_2_1_4_1","volume-title":"EMNIST: Extending MNIST to handwritten letters. In 2017 IJCNN. 2921--2926.","year":"2017","unstructured":"Gregory Cohen et al. 2017. EMNIST: Extending MNIST to handwritten letters. In 2017 IJCNN. 2921--2926."},{"key":"e_1_3_2_1_5_1","unstructured":"coral.ai. 2020. Edge TPU Documentation. https:\/\/coral.ai\/docs\/"},{"key":"e_1_3_2_1_6_1","unstructured":"developer.nvidia.com\/embedded\/jetson modules. 2020. Jetson Modules."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2749469.2750389"},{"key":"e_1_3_2_1_9_1","unstructured":"Gaurav Kumar et al. [n. d.]. Arrhythmia Detection in ECG Signals Using a MLP Network. ([n. d.])."},{"key":"e_1_3_2_1_10_1","volume-title":"MICRO '52","unstructured":"Hyoukjun Kwon et al. 2019. Understanding Reuse, Performance, and Hardware Cost of DNN Dataflow: A Data-Centric Approach. In MICRO '52. 754--768."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/A-SSCC47793.2019.9056967"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/51.932724"},{"key":"e_1_3_2_1_14_1","volume-title":"ISPASS","year":"2020","unstructured":"Ananda Samajdar et al. 2020. A Systematic Methodology for Characterizing Scalability of DNN Accelerators using SCALE-Sim. In ISPASS 2020. 58--68."},{"key":"e_1_3_2_1_15_1","volume-title":"TPU: Tensor-Processing-Unit. https:\/\/github.com\/scalesim-project\/scale-sim-v2\/tree\/main\/code-examples\/systolic-array-rtl.","year":"2021","unstructured":"Ananda Samajdar et al. 2021. TPU: Tensor-Processing-Unit. https:\/\/github.com\/scalesim-project\/scale-sim-v2\/tree\/main\/code-examples\/systolic-array-rtl."},{"key":"e_1_3_2_1_16_1","volume-title":"Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip","year":"2020","unstructured":"Bo Wang et al. 2020. Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip. In IEEE DATE."},{"key":"e_1_3_2_1_17_1","unstructured":"Jason Yosinski et al. 2014. How transferable are features in deep neural networks? CoRR abs\/1411.1792 (2014). arXiv:1411.1792 http:\/\/arxiv.org\/abs\/1411.1792"},{"key":"e_1_3_2_1_18_1","volume-title":"VLSI","author":"Yuan Z.","year":"2018","unstructured":"Z. Yuan et al. 2018. Sticker: A 0.41-62.1 TOPS\/W 8Bit Neural Network Processor with Multi-Sparsity Compatible Convolution Arrays and Online Tuning Acceleration for Fully Connected Layers. In VLSI 2018."}],"event":{"name":"DAC '22: 59th ACM\/IEEE Design Automation Conference","location":"San Francisco California","acronym":"DAC '22","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE CEDA"]},"container-title":["Proceedings of the 59th ACM\/IEEE Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3489517.3530406","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3489517.3530406","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:39Z","timestamp":1750191519000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3489517.3530406"}},"subtitle":["a heterogeneous reconfigurable neural network accelerator with software-configurable NoCs for training and inference on wearables"],"short-title":[],"issued":{"date-parts":[[2022,7,10]]},"references-count":17,"alternative-id":["10.1145\/3489517.3530406","10.1145\/3489517"],"URL":"https:\/\/doi.org\/10.1145\/3489517.3530406","relation":{},"subject":[],"published":{"date-parts":[[2022,7,10]]},"assertion":[{"value":"2022-08-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}