{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:21:02Z","timestamp":1750220462380,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T00:00:00Z","timestamp":1605484800000},"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":[],"published-print":{"date-parts":[[2020,11,16]]},"DOI":"10.1145\/3417313.3429380","type":"proceedings-article","created":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T16:05:52Z","timestamp":1604592352000},"page":"21-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Pushing the Envelope of Dynamic Spatial Gating technologies"],"prefix":"10.1145","author":[{"given":"Xueqin","family":"Huang","sequence":"first","affiliation":[{"name":"Texas A & M University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Urmish","family":"Thakker","sequence":"additional","affiliation":[{"name":"Arm ML Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dibakar","family":"Gope","sequence":"additional","affiliation":[{"name":"Arm ML Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesse","family":"Beu","sequence":"additional","affiliation":[{"name":"Arm ML Research"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,11,16]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Jeff Sieracki, Urmish Thakker, Marian Verhelst, and Poonam Yadav.","author":"Banbury Colby R.","year":"2020","unstructured":"Colby R. Banbury , V. Reddi , M. Lam , W. Fu , A. Fazel , J. Holleman , X. Huang , R. Hurtado , D. Kanter , Anton Lokhmotov , D. Patterson , D. Pau , Jae sun Seo , Jeff Sieracki, Urmish Thakker, Marian Verhelst, and Poonam Yadav. 2020 . Benchmarking TinyML Systems: Challenges and Direction. ArXiv abs\/2003.04821 (2020). Colby R. Banbury, V. Reddi, M. Lam, W. Fu, A. Fazel, J. Holleman, X. Huang, R. Hurtado, D. Kanter, Anton Lokhmotov, D. Patterson, D. Pau, Jae sun Seo, Jeff Sieracki, Urmish Thakker, Marian Verhelst, and Poonam Yadav. 2020. Benchmarking TinyML Systems: Challenges and Direction. ArXiv abs\/2003.04821 (2020)."},{"key":"e_1_3_2_1_2_1","volume-title":"Batch-Shaping for Learning Conditional Channel Gated Networks. arXiv","author":"Bejnordi Babak Ehteshami","year":"1907","unstructured":"Babak Ehteshami Bejnordi , Tijmen Blankevoort , and Max Welling . 2019. Batch-Shaping for Learning Conditional Channel Gated Networks. arXiv : 1907 .06627 [cs.LG] Babak Ehteshami Bejnordi, Tijmen Blankevoort, and Max Welling. 2019. Batch-Shaping for Learning Conditional Channel Gated Networks. arXiv: 1907.06627 [cs.LG]"},{"key":"e_1_3_2_1_3_1","unstructured":"Francesco Petrogalli Dan Andrei Iliescu. 2018 (accessed September 3 2020). Arm Scalable Vector Extensions and application to Machine Learning. https:\/\/developer.arm.com\/solutions\/hpc\/resources\/hpc-white-papers\/arm-scalable-vector-extensions-and-application-to-machine-learning  Francesco Petrogalli Dan Andrei Iliescu. 2018 (accessed September 3 2020). Arm Scalable Vector Extensions and application to Machine Learning. https:\/\/developer.arm.com\/solutions\/hpc\/resources\/hpc-white-papers\/arm-scalable-vector-extensions-and-application-to-machine-learning"},{"key":"e_1_3_2_1_4_1","volume-title":"Dynamic Channel Pruning: Feature Boosting and Suppression. arXiv","author":"Gao Xitong","year":"1810","unstructured":"Xitong Gao , Yiren Zhao , \u0141ukasz Dudziak , Robert Mullins , and Cheng zhong Xu . 2018. Dynamic Channel Pruning: Feature Boosting and Suppression. arXiv : 1810 .05331 [cs.CV] Xitong Gao, Yiren Zhao, \u0141ukasz Dudziak, Robert Mullins, and Cheng zhong Xu. 2018. Dynamic Channel Pruning: Feature Boosting and Suppression. arXiv: 1810.05331 [cs.CV]"},{"key":"e_1_3_2_1_5_1","volume-title":"High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands. arXiv","author":"Gope Dibakar","year":"2008","unstructured":"Dibakar Gope , Jesse Beu , and Matthew Mattina . 2020. High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands. arXiv : 2008 .00638 [cs.LG] Dibakar Gope, Jesse Beu, and Matthew Mattina. 2020. High Throughput Matrix-Matrix Multiplication between Asymmetric Bit-Width Operands. arXiv: 2008.00638 [cs.LG]"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00362"},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of Machine Learning and Systems","author":"Gope Dibakar","year":"2019","unstructured":"Dibakar Gope , Ganesh Dasika , and Matthew Mattina . 2019 . Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications . In Proceedings of Machine Learning and Systems 2019. 190--200. Dibakar Gope, Ganesh Dasika, and Matthew Mattina. 2019. Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications. In Proceedings of Machine Learning and Systems 2019. 190--200."},{"key":"e_1_3_2_1_8_1","volume":"201","author":"Han Song","unstructured":"Song Han , Huizi Mao , and William J. Dally. 201 5. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. arXiv: 1510.00149 [cs.CV] Song Han, Huizi Mao, and William J. Dally. 2015. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding. arXiv: 1510.00149 [cs.CV]","journal-title":"William J. Dally."},{"key":"e_1_3_2_1_9_1","unstructured":"Yihui He Xiangyu Zhang and Jian Sun. 2017. Channel Pruning for Accelerating Very Deep Neural Networks. arXiv: 1707.06168 [cs.CV]  Yihui He Xiangyu Zhang and Jian Sun. 2017. Channel Pruning for Accelerating Very Deep Neural Networks. arXiv: 1707.06168 [cs.CV]"},{"key":"e_1_3_2_1_10_1","unstructured":"Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv: 1704.04861 [cs.CV]  Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv: 1704.04861 [cs.CV]"},{"key":"e_1_3_2_1_11_1","unstructured":"Jie Hu Li Shen Samuel Albanie Gang Sun and Enhua Wu. 2017. Squeeze-and-Excitation Networks. arXiv: 1709.01507 [cs.CV]  Jie Hu Li Shen Samuel Albanie Gang Sun and Enhua Wu. 2017. Squeeze-and-Excitation Networks. arXiv: 1709.01507 [cs.CV]"},{"key":"e_1_3_2_1_12_1","volume-title":"Zhiru Zhang, and G. Edward Suh.","author":"Hua Weizhe","year":"2018","unstructured":"Weizhe Hua , Yuan Zhou , Christopher De Sa , Zhiru Zhang, and G. Edward Suh. 2018 . Channel Gating Neural Networks . arXiv: 1805.12549 [cs.LG] Weizhe Hua, Yuan Zhou, Christopher De Sa, Zhiru Zhang, and G. Edward Suh. 2018. Channel Gating Neural Networks. arXiv: 1805.12549 [cs.LG]"},{"key":"e_1_3_2_1_13_1","volume-title":"Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. CoRR abs\/1609.07061","author":"Hubara Itay","year":"2016","unstructured":"Itay Hubara , Matthieu Courbariaux , Daniel Soudry , Ran El-Yaniv , and Yoshua Bengio . 2016. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. CoRR abs\/1609.07061 ( 2016 ). arXiv:1609.07061 http:\/\/arxiv.org\/abs\/1609.07061 Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations. CoRR abs\/1609.07061 (2016). arXiv:1609.07061 http:\/\/arxiv.org\/abs\/1609.07061"},{"key":"e_1_3_2_1_14_1","unstructured":"Forrest N. Iandola Song Han Matthew W. Moskewicz Khalid Ashraf William J. Dally and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv:1602.07360 [cs.CV]  Forrest N. Iandola Song Han Matthew W. Moskewicz Khalid Ashraf William J. Dally and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv:1602.07360 [cs.CV]"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Max Jaderberg Andrea Vedaldi and Andrew Zisserman. 2014. Speeding up Convolutional Neural Networks with Low Rank Expansions. arXiv:1405.3866 [cs.CV]  Max Jaderberg Andrea Vedaldi and Andrew Zisserman. 2014. Speeding up Convolutional Neural Networks with Low Rank Expansions. arXiv:1405.3866 [cs.CV]","DOI":"10.5244\/C.28.88"},{"volume-title":"2016 49th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO). 1--12","author":"Judd P.","key":"e_1_3_2_1_16_1","unstructured":"P. Judd , J. Albericio , T. Hetherington , T. M. Aamodt , and A. Moshovos . 2016. Stripes: Bit-serial deep neural network computing . In 2016 49th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO). 1--12 . P. Judd, J. Albericio, T. Hetherington, T. M. Aamodt, and A. Moshovos. 2016. Stripes: Bit-serial deep neural network computing. In 2016 49th Annual IEEE\/ACM International Symposium on Microarchitecture (MICRO). 1--12."},{"key":"e_1_3_2_1_17_1","unstructured":"Pavlo Molchanov Stephen Tyree Tero Karras Timo Aila and Jan Kautz. 2016. Pruning Convolutional Neural Networks for Resource Efficient Inference. arXiv:1611.06440 [cs.LG]  Pavlo Molchanov Stephen Tyree Tero Karras Timo Aila and Jan Kautz. 2016. Pruning Convolutional Neural Networks for Resource Efficient Inference. arXiv:1611.06440 [cs.LG]"},{"volume-title":"Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network. In 2018 ACM\/IEEE 45th Annual International Symposium on Computer Architecture (ISCA). 764--775","author":"Sharma H.","key":"e_1_3_2_1_18_1","unstructured":"H. Sharma , J. Park , N. Suda , L. Lai , B. Chau , V. Chandra , and H. Esmaeilzadeh . 2018 . Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network. In 2018 ACM\/IEEE 45th Annual International Symposium on Computer Architecture (ISCA). 764--775 . H. Sharma, J. Park, N. Suda, L. Lai, B. Chau, V. Chandra, and H. Esmaeilzadeh. 2018. Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network. In 2018 ACM\/IEEE 45th Annual International Symposium on Computer Architecture (ISCA). 764--775."},{"key":"e_1_3_2_1_19_1","unstructured":"Noam Shazeer Azalia Mirhoseini Krzysztof Maziarz Andy Davis Quoc Le Geoffrey Hinton and Jeff Dean. 2017. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. arXiv:1701.06538 [cs.LG]  Noam Shazeer Azalia Mirhoseini Krzysztof Maziarz Andy Davis Quoc Le Geoffrey Hinton and Jeff Dean. 2017. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. arXiv:1701.06538 [cs.LG]"},{"key":"e_1_3_2_1_20_1","volume-title":"Skipping RNN State Updates without Retraining the Original Model (SenSys-ML","author":"Tao Jin","year":"2019","unstructured":"Jin Tao , Urmish Thakker , Ganesh Dasika , and Jesse Beu . 2019. Skipping RNN State Updates without Retraining the Original Model (SenSys-ML 2019 ). Association for Computing Machinery , New York, NY, USA , 31--36. https:\/\/doi.org\/10.1145\/3362743.3362965 10.1145\/3362743.3362965 Jin Tao, Urmish Thakker, Ganesh Dasika, and Jesse Beu. 2019. Skipping RNN State Updates without Retraining the Original Model (SenSys-ML 2019). Association for Computing Machinery, New York, NY, USA, 31--36. https:\/\/doi.org\/10.1145\/3362743.3362965"},{"volume-title":"Run-Time Efficient RNN Compression for Inference on Edge Devices. In 2019 2nd Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2). 26--30","author":"Thakker U.","key":"e_1_3_2_1_21_1","unstructured":"U. Thakker , J. Beu , D. Gope , G. Dasika , and M. Mattina . 2019 . Run-Time Efficient RNN Compression for Inference on Edge Devices. In 2019 2nd Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2). 26--30 . U. Thakker, J. Beu, D. Gope, G. Dasika, and M. Mattina. 2019. Run-Time Efficient RNN Compression for Inference on Edge Devices. In 2019 2nd Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2). 26--30."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Urmish Thakker Jesse Beu Dibakar Gope Ganesh Dasika and Matthew Mattina. 2020. Rank and run-time aware compression of NLP Applications. arXiv:2010.03193 [cs.CL]  Urmish Thakker Jesse Beu Dibakar Gope Ganesh Dasika and Matthew Mattina. 2020. Rank and run-time aware compression of NLP Applications. arXiv:2010.03193 [cs.CL]","DOI":"10.18653\/v1\/2020.sustainlp-1.2"},{"key":"e_1_3_2_1_23_1","volume-title":"Compressing RNNs for IoT devices by 15--38x using Kronecker Products. CoRR abs\/1906.02876","author":"Thakker Urmish","year":"2019","unstructured":"Urmish Thakker , Jesse G. Beu , Dibakar Gope , Chu Zhou , Igor Fedorov , Ganesh Dasika , and Matthew Mattina . 2019. Compressing RNNs for IoT devices by 15--38x using Kronecker Products. CoRR abs\/1906.02876 ( 2019 ). arXiv:1906.02876 http:\/\/arxiv.org\/abs\/1906.02876 Urmish Thakker, Jesse G. Beu, Dibakar Gope, Chu Zhou, Igor Fedorov, Ganesh Dasika, and Matthew Mattina. 2019. Compressing RNNs for IoT devices by 15--38x using Kronecker Products. CoRR abs\/1906.02876 (2019). arXiv:1906.02876 http:\/\/arxiv.org\/abs\/1906.02876"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Urmish Thakker Igor Fedorov Jesse Beu Dibakar Gope Chu Zhou Ganesh Dasika and Matthew Mattina. 2019. Pushing the limits of RNN Compression. arXiv:1910.02558 [cs.LG]  Urmish Thakker Igor Fedorov Jesse Beu Dibakar Gope Chu Zhou Ganesh Dasika and Matthew Mattina. 2019. Pushing the limits of RNN Compression. arXiv:1910.02558 [cs.LG]","DOI":"10.1109\/EMC2-NIPS53020.2019.00012"},{"key":"e_1_3_2_1_25_1","unstructured":"Urmish Thakker Paul Whatmough Matthew Mattina and Jesse Beu. 2020. Compressing Language Models using Doped Kronecker Products. arXiv:2001.08896 [cs.LG]  Urmish Thakker Paul Whatmough Matthew Mattina and Jesse Beu. 2020. Compressing Language Models using Doped Kronecker Products. arXiv:2001.08896 [cs.LG]"},{"volume-title":"Proceedings of the European Conference on Computer Vision (ECCV).","author":"Wang Xin","key":"e_1_3_2_1_26_1","unstructured":"Xin Wang , Fisher Yu , Zi-Yi Dou , Trevor Darrell , and Joseph E. Gonzalez . 2018. SkipNet: Learning Dynamic Routing in Convolutional Networks . In Proceedings of the European Conference on Computer Vision (ECCV). Xin Wang, Fisher Yu, Zi-Yi Dou, Trevor Darrell, and Joseph E. Gonzalez. 2018. SkipNet: Learning Dynamic Routing in Convolutional Networks. In Proceedings of the European Conference on Computer Vision (ECCV)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Xiangyu Zhang Xinyu Zhou Mengxiao Lin and Jian Sun. 2017. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. arXiv:1707.01083 [cs.CV]  Xiangyu Zhang Xinyu Zhou Mengxiao Lin and Jian Sun. 2017. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. arXiv:1707.01083 [cs.CV]","DOI":"10.1109\/CVPR.2018.00716"},{"key":"e_1_3_2_1_28_1","volume-title":"Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations. arXiv:2002.07136 [cs.CV]","author":"Zhang Yichi","year":"2020","unstructured":"Yichi Zhang , Ritchie Zhao , Weizhe Hua , Nayun Xu , G. Edward Suh , and Zhiru Zhang . 2020 . Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations. arXiv:2002.07136 [cs.CV] Yichi Zhang, Ritchie Zhao, Weizhe Hua, Nayun Xu, G. Edward Suh, and Zhiru Zhang. 2020. Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations. arXiv:2002.07136 [cs.CV]"}],"event":{"name":"SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems","sponsor":["SIGMETRICS ACM Special Interest Group on Measurement and Evaluation","SIGCOMM ACM Special Interest Group on Data Communication","SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems","SIGBED ACM Special Interest Group on Embedded Systems","SIGARCH ACM Special Interest Group on Computer Architecture"],"location":"Virtual Event Japan","acronym":"SenSys '20"},"container-title":["Proceedings of the 2nd International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3417313.3429380","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3417313.3429380","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:48:14Z","timestamp":1750193294000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3417313.3429380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,16]]},"references-count":28,"alternative-id":["10.1145\/3417313.3429380","10.1145\/3417313"],"URL":"https:\/\/doi.org\/10.1145\/3417313.3429380","relation":{},"subject":[],"published":{"date-parts":[[2020,11,16]]},"assertion":[{"value":"2020-11-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}