{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T18:37:21Z","timestamp":1775155041053,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":95,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"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":[[2023,6,13]]},"DOI":"10.1145\/3589610.3596278","type":"proceedings-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T15:13:34Z","timestamp":1686669214000},"page":"26-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["MinUn: Accurate ML Inference on Microcontrollers"],"prefix":"10.1145","author":[{"given":"Shikhar","family":"Jaiswal","sequence":"first","affiliation":[{"name":"Microsoft Research, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahul Kranti Kiran","family":"Goli","sequence":"additional","affiliation":[{"name":"Microsoft Research, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aayan","family":"Kumar","sequence":"additional","affiliation":[{"name":"Microsoft Research, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivek","family":"Seshadri","sequence":"additional","affiliation":[{"name":"Microsoft Research, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rahul","family":"Sharma","sequence":"additional","affiliation":[{"name":"Microsoft Research, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Martin Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry Moore Derek G. Murray Benoit Steiner Paul Tucker Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. https:\/\/github.com\/tensorflow\/tensorflow\/blob\/master\/tensorflow\/core\/framework\/bfloat16.cc \t\t\t\t  Martin Abadi Paul Barham Jianmin Chen Zhifeng Chen Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Geoffrey Irving Michael Isard Manjunath Kudlur Josh Levenberg Rajat Monga Sherry Moore Derek G. Murray Benoit Steiner Paul Tucker Vijay Vasudevan Pete Warden Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. https:\/\/github.com\/tensorflow\/tensorflow\/blob\/master\/tensorflow\/core\/framework\/bfloat16.cc"},{"key":"e_1_3_2_1_2_1","volume-title":"Ullman","author":"Aho Alfred V.","year":"2006","unstructured":"Alfred V. Aho , Monica S. Lam , Ravi Sethi , and Jeffrey D . Ullman . 2006 . Compilers : Principles, Techniques, and Tools (2nd Edition). Addison-Wesley Longman Publishing Co. , Inc., USA. isbn:0321486811 Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman. 2006. Compilers: Principles, Techniques, and Tools (2nd Edition). Addison-Wesley Longman Publishing Co., Inc., USA. isbn:0321486811"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2010.04.019"},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the 4th International Conference on Ambient Assisted Living and Home Care (IWAAL\u201912)","author":"Anguita Davide","unstructured":"Davide Anguita , Alessandro Ghio , Luca Oneto , Xavier Parra , and Jorge L . Reyes-Ortiz. 2012. Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine . In Proceedings of the 4th International Conference on Ambient Assisted Living and Home Care (IWAAL\u201912) . Springer-Verlag, Berlin, Heidelberg. 216\u2013223. isbn:9783642353949 https:\/\/archive.ics.uci.edu\/ml\/datasets\/human+activity+recognition+using+smartphones Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, and Jorge L. Reyes-Ortiz. 2012. Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine. In Proceedings of the 4th International Conference on Ambient Assisted Living and Home Care (IWAAL\u201912). Springer-Verlag, Berlin, Heidelberg. 216\u2013223. isbn:9783642353949 https:\/\/archive.ics.uci.edu\/ml\/datasets\/human+activity+recognition+using+smartphones"},{"key":"e_1_3_2_1_5_1","unstructured":"ARM. 2021. ARM Cortex-M official website. https:\/\/developer.arm.com\/ip-products\/processors\/cortex-m \t\t\t\t  ARM. 2021. ARM Cortex-M official website. https:\/\/developer.arm.com\/ip-products\/processors\/cortex-m"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/581888.581891"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/FPGA.1999.803669"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1809028.1806620"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/FPGA.2003.1227262"},{"key":"e_1_3_2_1_10_1","volume-title":"Getting started with Arduino: the open source electronics prototyping platform. Maker Media","author":"Banzi Massimo","unstructured":"Massimo Banzi and Michael Shiloh . 2014. Getting started with Arduino: the open source electronics prototyping platform. Maker Media , Inc .. Massimo Banzi and Michael Shiloh. 2014. Getting started with Arduino: the open source electronics prototyping platform. Maker Media, Inc.."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.14311\/692"},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the Fifth International Symposium on High-Performance Computer Architecture","author":"David","year":"1999","unstructured":"David M. Brooks and Margaret Martonosi. 1999. Dynamically Exploiting Narrow Width Operands to Improve Processor Power and Performance . In Proceedings of the Fifth International Symposium on High-Performance Computer Architecture , Orlando, FL, USA , January 9-12, 1999 . Association for Computing Machinery, New York, NY, USA. 13\u201322. David M. Brooks and Margaret Martonosi. 1999. Dynamically Exploiting Narrow Width Operands to Improve Processor Power and Performance. In Proceedings of the Fifth International Symposium on High-Performance Computer Architecture, Orlando, FL, USA, January 9-12, 1999. Association for Computing Machinery, New York, NY, USA. 13\u201322."},{"key":"e_1_3_2_1_13_1","volume-title":"Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332.","author":"Cai Han","year":"2018","unstructured":"Han Cai , Ligeng Zhu , and Song Han . 2018 . Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332. Han Cai, Ligeng Zhu, and Song Han. 2018. Proxylessnas: Direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332."},{"key":"e_1_3_2_1_14_1","volume-title":"Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc","author":"Chen Shangyu","unstructured":"Shangyu Chen , Wenya Wang , and Sinno Jialin Pan . 2019. MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization . In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc , E. Fox, and R. Garnett (Eds.). Curran Associates, Inc. , 3916\u20133926. http:\/\/papers.nips.cc\/paper\/8647-metaquant-learning-to-quantize-by-learning-to-penetrate-non-differentiable-quantization.pdf Shangyu Chen, Wenya Wang, and Sinno Jialin Pan. 2019. MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 3916\u20133926. http:\/\/papers.nips.cc\/paper\/8647-metaquant-learning-to-quantize-by-learning-to-penetrate-non-differentiable-quantization.pdf"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966159"},{"key":"e_1_3_2_1_16_1","volume-title":"BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. CoRR, abs\/1602.02830","author":"Courbariaux Matthieu","year":"2016","unstructured":"Matthieu Courbariaux and Yoshua Bengio . 2016. BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. CoRR, abs\/1602.02830 ( 2016 ), arxiv:1602.02830. arxiv:1602.02830 Matthieu Courbariaux and Yoshua Bengio. 2016. BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1. CoRR, abs\/1602.02830 (2016), arxiv:1602.02830. arxiv:1602.02830"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2535838.2535874"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3014426"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/2555754.2555776"},{"key":"e_1_3_2_1_20_1","volume-title":"Character Recognition in Natural Images. In VISAPP 2009 - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications","volume":"2","author":"de Campos Te\u00f3filo Em\u00eddio","year":"2009","unstructured":"Te\u00f3filo Em\u00eddio de Campos , Bodla Rakesh Babu , and Manik Varma . 2009 . Character Recognition in Natural Images. In VISAPP 2009 - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications , Lisboa, Portugal , February 5-8, 2009 - Volume 2 . INSTICC Press, Portugal. 273\u2013280. https:\/\/www.researchgate.net\/publication\/221416071_Character_Recognition_in_Natural_Images Te\u00f3filo Em\u00eddio de Campos, Bodla Rakesh Babu, and Manik Varma. 2009. Character Recognition in Natural Images. In VISAPP 2009 - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, Lisboa, Portugal, February 5-8, 2009 - Volume 2. INSTICC Press, Portugal. 273\u2013280. https:\/\/www.researchgate.net\/publication\/221416071_Character_Recognition_in_Natural_Images"},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the Thirty-second Annual Conference on Neural Information Processing Systems (NeurIPS).","author":"Dennis Don","year":"2019","unstructured":"Don Dennis , Durmus Alp Emre Acar , Vikram Mandikal , Vinu Sankar Sadasivan , Harsha Vardhan Simhadri , Venkatesh Saligrama , and Prateek Jain . 2019 . Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices . In Proceedings of the Thirty-second Annual Conference on Neural Information Processing Systems (NeurIPS). Don Dennis, Durmus Alp Emre Acar, Vikram Mandikal, Vinu Sankar Sadasivan, Harsha Vardhan Simhadri, Venkatesh Saligrama, and Prateek Jain. 2019. Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices. In Proceedings of the Thirty-second Annual Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the Thirty-first Annual Conference on Neural Information Processing Systems (NeurIPS). 10976\u201310987","author":"Dennis Don","year":"2018","unstructured":"Don Dennis , Chirag Pabbaraju , Harsha Vardhan Simhadri , and Prateek Jain . 2018 . Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices . In Proceedings of the Thirty-first Annual Conference on Neural Information Processing Systems (NeurIPS). 10976\u201310987 . all_papers\/DennisPSJ18.pdf slides\/DennisPSJ18.pdf Don Dennis, Chirag Pabbaraju, Harsha Vardhan Simhadri, and Prateek Jain. 2018. Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices. In Proceedings of the Thirty-first Annual Conference on Neural Information Processing Systems (NeurIPS). 10976\u201310987. all_papers\/DennisPSJ18.pdf slides\/DennisPSJ18.pdf"},{"key":"e_1_3_2_1_23_1","volume-title":"Heterogeneous Bitwidth Binarization in Convolutional Neural Networks. CoRR, abs\/1805.10368","author":"Fromm Josh","year":"2018","unstructured":"Josh Fromm , Shwetak Patel , and Matthai Philipose . 2018. Heterogeneous Bitwidth Binarization in Convolutional Neural Networks. CoRR, abs\/1805.10368 ( 2018 ), arxiv:1805.10368. arxiv:1805.10368 Josh Fromm, Shwetak Patel, and Matthai Philipose. 2018. Heterogeneous Bitwidth Binarization in Convolutional Neural Networks. CoRR, abs\/1805.10368 (2018), arxiv:1805.10368. arxiv:1805.10368"},{"key":"e_1_3_2_1_24_1","volume-title":"MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications. In 31st International Conference on Field-Programmable Logic and Applications, FPL 2021","author":"Ghanathe Nikhil Pratap","year":"2021","unstructured":"Nikhil Pratap Ghanathe , Vivek Seshadri , Rahul Sharma , Steve Wilton , and Aayan Kumar . 2021 . MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications. In 31st International Conference on Field-Programmable Logic and Applications, FPL 2021 , Dresden, Germany, August 30 - Sept. 3, 2021. 347\u2013354. Nikhil Pratap Ghanathe, Vivek Seshadri, Rahul Sharma, Steve Wilton, and Aayan Kumar. 2021. MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications. In 31st International Conference on Field-Programmable Logic and Applications, FPL 2021, Dresden, Germany, August 30 - Sept. 3, 2021. 347\u2013354."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00495"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3314221.3314597"},{"key":"e_1_3_2_1_27_1","unstructured":"Posit Working Group. 2018. Posit Standard Documentation. https:\/\/posithub.org\/docs\/posit_standard.pdf \t\t\t\t  Posit Working Group. 2018. Posit Standard Documentation. https:\/\/posithub.org\/docs\/posit_standard.pdf"},{"key":"e_1_3_2_1_28_1","volume-title":"Gudovskiy and Luca Rigazio","author":"Denis","year":"2017","unstructured":"Denis A. Gudovskiy and Luca Rigazio . 2017 . ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks. CoRR , abs\/1706.02393 (2017), https:\/\/www.researchgate.net\/publication\/317419072_ShiftCNN_Generalized_Low-Precision_Architecture_for_Inference_of_Convolutional_Neural_Networks Denis A. Gudovskiy and Luca Rigazio. 2017. ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks. CoRR, abs\/1706.02393 (2017), https:\/\/www.researchgate.net\/publication\/317419072_ShiftCNN_Generalized_Low-Precision_Architecture_for_Inference_of_Convolutional_Neural_Networks"},{"key":"e_1_3_2_1_29_1","volume-title":"International Conference on Machine Learning. PMLR, International Convention Centre","author":"Gupta Chirag","year":"2017","unstructured":"Chirag Gupta , Arun Sai Suggala , Ankit Goyal , Harsha Vardhan Simhadri , Bhargavi Paranjape , Ashish Kumar , Saurabh Goyal , Raghavendra Udupa , Manik Varma , and Prateek Jain . 2017 . ProtoNN: compressed and accurate kNN for resource-scarce devices . In International Conference on Machine Learning. PMLR, International Convention Centre , Sydney, Australia. 1331\u20131340. https:\/\/dl.acm.org\/doi\/10.5555\/3305381.3305519 Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, and Prateek Jain. 2017. ProtoNN: compressed and accurate kNN for resource-scarce devices. In International Conference on Machine Learning. PMLR, International Convention Centre, Sydney, Australia. 1331\u20131340. https:\/\/dl.acm.org\/doi\/10.5555\/3305381.3305519"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.14529\/jsfi170206"},{"key":"e_1_3_2_1_31_1","unstructured":"John Gustafson. 2017. Posit Arithmetic. https:\/\/posithub.org\/docs\/Posits4.pdf \t\t\t\t  John Gustafson. 2017. Posit Arithmetic. https:\/\/posithub.org\/docs\/Posits4.pdf"},{"key":"e_1_3_2_1_32_1","volume-title":"LFFD: A Light and Fast Face Detector for Edge Devices. In arXiv:1904.10633.","author":"He Yonghao","year":"2019","unstructured":"Yonghao He , Dezhong Xu , Lifang Wu , Meng Jian , Shiming Xiang , and Chunhong Pan . 2019 . LFFD: A Light and Fast Face Detector for Edge Devices. In arXiv:1904.10633. Yonghao He, Dezhong Xu, Lifang Wu, Meng Jian, Shiming Xiang, and Chunhong Pan. 2019. LFFD: A Light and Fast Face Detector for Edge Devices. In arXiv:1904.10633."},{"key":"e_1_3_2_1_33_1","volume-title":"Channel Pruning for Accelerating Very Deep Neural Networks. In IEEE International Conference on Computer Vision, ICCV 2017","author":"He Yihui","year":"2017","unstructured":"Yihui He , Xiangyu Zhang , and Jian Sun . 2017 . Channel Pruning for Accelerating Very Deep Neural Networks. In IEEE International Conference on Computer Vision, ICCV 2017 , Venice, Italy , October 22-29, 2017. 1398\u20131406. Yihui He, Xiangyu Zhang, and Jian Sun. 2017. Channel Pruning for Accelerating Very Deep Neural Networks. In IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017. 1398\u20131406."},{"key":"e_1_3_2_1_34_1","volume-title":"Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network Using Truncated Gaussian Approximation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). arxiv:1810","author":"He Zhezhi","year":"2019","unstructured":"Zhezhi He and Deliang Fan . 2019 . Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network Using Truncated Gaussian Approximation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). arxiv:1810 .01018 Zhezhi He and Deliang Fan. 2019. Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network Using Truncated Gaussian Approximation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). arxiv:1810.01018"},{"key":"e_1_3_2_1_35_1","volume-title":"Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc","author":"Hou Lu","unstructured":"Lu Hou , Jinhua Zhu , James Kwok , Fei Gao , Tao Qin , and Tie-Yan Liu . 2019. Normalization Helps Training of Quantized LSTM . In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc , E. Fox, and R. Garnett (Eds.). Curran Associates, Inc. , 7346\u20137356. http:\/\/papers.nips.cc\/paper\/8954-normalization-helps-training-of-quantized-lstm.pdf Lu Hou, Jinhua Zhu, James Kwok, Fei Gao, Tao Qin, and Tie-Yan Liu. 2019. Normalization Helps Training of Quantized LSTM. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 7346\u20137356. http:\/\/papers.nips.cc\/paper\/8954-normalization-helps-training-of-quantized-lstm.pdf"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.991427"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_38_1","volume-title":"Advances in Neural Information Processing Systems 29","author":"Hubara Itay","unstructured":"Itay Hubara , Matthieu Courbariaux , Daniel Soudry , Ran El-Yaniv , and Yoshua Bengio . 2016. Binarized Neural Networks . In Advances in Neural Information Processing Systems 29 , D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett (Eds.). Curran Associates, Inc. , 4107\u20134115. http:\/\/papers.nips.cc\/paper\/6573-binarized-neural-networks.pdf Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016. Binarized Neural Networks. In Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett (Eds.). Curran Associates, Inc., 4107\u20134115. http:\/\/papers.nips.cc\/paper\/6573-binarized-neural-networks.pdf"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"e_1_3_2_1_40_1","volume-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 1MB model size. CoRR, abs\/1602.07360","author":"Iandola Forrest N.","year":"2016","unstructured":"Forrest N. Iandola , Matthew W. Moskewicz , Khalid Ashraf , Song Han , William J. Dally , and Kurt Keutzer . 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 1MB model size. CoRR, abs\/1602.07360 ( 2016 ), arxiv:1602.07360v1 Forrest N. Iandola, Matthew W. Moskewicz, Khalid Ashraf, Song Han, William J. Dally, and Kurt Keutzer. 2016. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 1MB model size. CoRR, abs\/1602.07360 (2016), arxiv:1602.07360v1"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_3_2_1_42_1","volume-title":"Sarah Knepper, Mourad Gouicem, Richard Winterton, David Mansell, Andreas Gal, Alexey Frunze, and Alexey Frunze.","author":"Jacob Benoit","year":"2015","unstructured":"Benoit Jacob , Pete Warden , Miao Wang , David Andersen , Maciek Chociej , Justine Tunney , Mark J. Matthews , Marie White , Suharsh Sivakumar , Sagi Marcovich , Murat Efe Guney , Sarah Knepper, Mourad Gouicem, Richard Winterton, David Mansell, Andreas Gal, Alexey Frunze, and Alexey Frunze. 2015 . gemmlowp: a small self-contained low-precision gemm library. https:\/\/github.com\/google\/gemmlowp Benoit Jacob, Pete Warden, Miao Wang, David Andersen, Maciek Chociej, Justine Tunney, Mark J. Matthews, Marie White, Suharsh Sivakumar, Sagi Marcovich, Murat Efe Guney, Sarah Knepper, Mourad Gouicem, Richard Winterton, David Mansell, Andreas Gal, Alexey Frunze, and Alexey Frunze. 2015. gemmlowp: a small self-contained low-precision gemm library. https:\/\/github.com\/google\/gemmlowp"},{"key":"e_1_3_2_1_43_1","volume-title":"Aayan Kumar, Vivek Seshadri, and Rahul Sharma.","author":"Jaiswal Shikhar","year":"2022","unstructured":"Shikhar Jaiswal , Rahul Kiran Kranti Goli , Aayan Kumar, Vivek Seshadri, and Rahul Sharma. 2022 . MinUn: Accurate ML Inference on Microcontrollers . arxiv:2210.16556. Shikhar Jaiswal, Rahul Kiran Kranti Goli, Aayan Kumar, Vivek Seshadri, and Rahul Sharma. 2022. MinUn: Accurate ML Inference on Microcontrollers. arxiv:2210.16556."},{"key":"e_1_3_2_1_44_1","volume-title":"Prateek Jain, and Rahul Sharma.","author":"Jaiswal Shikhar","year":"2021","unstructured":"Shikhar Jaiswal , Oindrila Saha , Aayan Kumar , Harsha Vardhan Simhadri , Prateek Jain, and Rahul Sharma. 2021 . Enabling Accurate Computer Vision on Tiny Microcontrollers with RNNPool Operator and SeeDot Compiler . https:\/\/towardsdatascience.com\/enabling-accurate-computer-vision-on-tiny-microcontrollers-with-rnnpool-operator-and-seedot-d6944930dcf9 Shikhar Jaiswal, Oindrila Saha, Aayan Kumar, Harsha Vardhan Simhadri, Prateek Jain, and Rahul Sharma. 2021. Enabling Accurate Computer Vision on Tiny Microcontrollers with RNNPool Operator and SeeDot Compiler. https:\/\/towardsdatascience.com\/enabling-accurate-computer-vision-on-tiny-microcontrollers-with-rnnpool-operator-and-seedot-d6944930dcf9"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1088\/0967-3334\/25\/5\/007"},{"key":"e_1_3_2_1_46_1","volume-title":"Rethinking floating point for deep learning. CoRR, abs\/1811.01721","author":"Johnson Jeff","year":"2018","unstructured":"Jeff Johnson . 2018. Rethinking floating point for deep learning. CoRR, abs\/1811.01721 ( 2018 ), arxiv:1811.01721 Jeff Johnson. 2018. Rethinking floating point for deep learning. CoRR, abs\/1811.01721 (2018), arxiv:1811.01721"},{"key":"e_1_3_2_1_47_1","volume-title":"abs\/cs\/0011047","author":"Knuth Don E.","year":"2000","unstructured":"Don E. Knuth . 2000. Dancing Links . CoRR , abs\/cs\/0011047 ( 2000 ), arxiv:cs\/0011047 Don E. Knuth. 2000. Dancing Links. CoRR, abs\/cs\/0011047 (2000), arxiv:cs\/0011047"},{"key":"e_1_3_2_1_48_1","volume-title":"Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks. CoRR, abs\/1711.02213","author":"K\u00f6ster Urs","year":"2017","unstructured":"Urs K\u00f6ster , Tristan Webb , Xin Wang , Marcel Nassar , Arjun K. Bansal , William Constable , Oguz Elibol , Stewart Hall , Luke Hornof , Amir Khosrowshahi , Carey Kloss , Ruby J. Pai , and Naveen Rao . 2017 . Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks. CoRR, abs\/1711.02213 (2017), arxiv:1711.02213 Urs K\u00f6ster, Tristan Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William Constable, Oguz Elibol, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, and Naveen Rao. 2017. Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks. CoRR, abs\/1711.02213 (2017), arxiv:1711.02213"},{"key":"e_1_3_2_1_49_1","volume-title":"Quantizing deep convolutional networks for efficient inference: A whitepaper. CoRR, abs\/1806.08342","author":"Krishnamoorthi Raghuraman","year":"2018","unstructured":"Raghuraman Krishnamoorthi . 2018. Quantizing deep convolutional networks for efficient inference: A whitepaper. CoRR, abs\/1806.08342 ( 2018 ), arxiv:1806.08342 Raghuraman Krishnamoorthi. 2018. Quantizing deep convolutional networks for efficient inference: A whitepaper. CoRR, abs\/1806.08342 (2018), arxiv:1806.08342"},{"key":"e_1_3_2_1_50_1","unstructured":"Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. Citeseer. \t\t\t\t  Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. Citeseer."},{"key":"e_1_3_2_1_51_1","volume-title":"Hinton","author":"Krizhevsky Alex","year":"2012","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E . Hinton . 2012 . ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States .. 1106\u20131114. http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States.. 1106\u20131114. http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks"},{"key":"e_1_3_2_1_52_1","volume-title":"International Conference on Machine Learning. PMLR, International Convention Centre","author":"Kumar Ashish","year":"2017","unstructured":"Ashish Kumar , Saurabh Goyal , and Manik Varma . 2017 . Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things . In International Conference on Machine Learning. PMLR, International Convention Centre , Sydney, Australia. 1935\u20131944. https:\/\/dl.acm.org\/doi\/10.5555\/3305381.3305581 Ashish Kumar, Saurabh Goyal, and Manik Varma. 2017. Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things. In International Conference on Machine Learning. PMLR, International Convention Centre, Sydney, Australia. 1935\u20131944. https:\/\/dl.acm.org\/doi\/10.5555\/3305381.3305581"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3428250"},{"key":"e_1_3_2_1_54_1","volume-title":"Proceedings of the Thirty-first Annual Conference on Neural Information Processing Systems (NeurIPS). 9031\u20139042","author":"Kusupati Aditya","year":"2018","unstructured":"Aditya Kusupati , Manish Singh , Kush Bhatia , Ashish Kumar , Prateek Jain , and Manik Varma . 2018 . FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network . In Proceedings of the Thirty-first Annual Conference on Neural Information Processing Systems (NeurIPS). 9031\u20139042 . https:\/\/dl.acm.org\/doi\/10.5555\/3327546.3327577 Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, and Manik Varma. 2018. FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network. In Proceedings of the Thirty-first Annual Conference on Neural Information Processing Systems (NeurIPS). 9031\u20139042. https:\/\/dl.acm.org\/doi\/10.5555\/3327546.3327577"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_56_1","unstructured":"Siew Hoon Leong. 2020. SoftPosit. https:\/\/gitlab.com\/cerlane\/SoftPosit \t\t\t\t  Siew Hoon Leong. 2020. SoftPosit. https:\/\/gitlab.com\/cerlane\/SoftPosit"},{"key":"e_1_3_2_1_57_1","first-page":"I","article-title":"Training Quantized Nets: A Deeper Understanding","volume":"30","author":"Li Hao","year":"2017","unstructured":"Hao Li , Soham De , Zheng Xu , Christoph Studer , Hanan Samet , and Tom Goldstein . 2017 . Training Quantized Nets: A Deeper Understanding . In Advances in Neural Information Processing Systems 30 , I . Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 5811\u20135821. http:\/\/papers.nips.cc\/paper\/7163-training-quantized-nets-a-deeper-understanding.pdf Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, and Tom Goldstein. 2017. Training Quantized Nets: A Deeper Understanding. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 5811\u20135821. http:\/\/papers.nips.cc\/paper\/7163-training-quantized-nets-a-deeper-understanding.pdf","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_58_1","volume-title":"Annual Conference on Neural Information Processing Systems (NeurIPS).","author":"Lin Ji","year":"2021","unstructured":"Ji Lin , Wei-Ming Chen , Han Cai , Chuang Gan , and Song Han . 2021 . MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning . In Annual Conference on Neural Information Processing Systems (NeurIPS). Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, and Song Han. 2021. MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning. In Annual Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_59_1","volume-title":"MCUNet: Tiny Deep Learning on IoT Devices. In Annual Conference on Neural Information Processing Systems (NeurIPS).","author":"Lin Ji","year":"2020","unstructured":"Ji Lin , Wei-Ming Chen , John Cohn , Chuang Gan , and Song Han . 2020 . MCUNet: Tiny Deep Learning on IoT Devices. In Annual Conference on Neural Information Processing Systems (NeurIPS). Ji Lin, Wei-Ming Chen, John Cohn, Chuang Gan, and Song Han. 2020. MCUNet: Tiny Deep Learning on IoT Devices. In Annual Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_60_1","volume-title":"Neural Networks with Few Multiplications. CoRR, abs\/1510.03009","author":"Lin Zhouhan","year":"2015","unstructured":"Zhouhan Lin , Matthieu Courbariaux , Roland Memisevic , and Yoshua Bengio . 2015. Neural Networks with Few Multiplications. CoRR, abs\/1510.03009 ( 2015 ), arxiv:1510.03009. arxiv:1510.03009 Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio. 2015. Neural Networks with Few Multiplications. CoRR, abs\/1510.03009 (2015), arxiv:1510.03009. arxiv:1510.03009"},{"key":"e_1_3_2_1_61_1","volume-title":"Relaxed Quantization for Discretized Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HkxjYoCqKX","author":"Louizos Christos","year":"2019","unstructured":"Christos Louizos , Matthias Reisser , Tijmen Blankevoort , Efstratios Gavves , and Max Welling . 2019 . Relaxed Quantization for Discretized Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HkxjYoCqKX Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, and Max Welling. 2019. Relaxed Quantization for Discretized Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HkxjYoCqKX"},{"key":"e_1_3_2_1_62_1","volume-title":"The European Conference on Computer Vision (ECCV). https:\/\/openaccess.thecvf.com\/content_ECCV_2018\/papers\/Julieta_Martinez_LSQ_lower_runtime_ECCV_2018_paper.pdf","author":"Martinez Julieta","unstructured":"Julieta Martinez , Shobhit Zakhmi , Holger H. Hoos , and James J. Little . 2018. LSQ++: Lower running time and higher recall in multi-codebook quantization . In The European Conference on Computer Vision (ECCV). https:\/\/openaccess.thecvf.com\/content_ECCV_2018\/papers\/Julieta_Martinez_LSQ_lower_runtime_ECCV_2018_paper.pdf Julieta Martinez, Shobhit Zakhmi, Holger H. Hoos, and James J. Little. 2018. LSQ++: Lower running time and higher recall in multi-codebook quantization. In The European Conference on Computer Vision (ECCV). https:\/\/openaccess.thecvf.com\/content_ECCV_2018\/papers\/Julieta_Martinez_LSQ_lower_runtime_ECCV_2018_paper.pdf"},{"key":"e_1_3_2_1_63_1","volume-title":"Same But Different - Recovering Neural Network Quantization Error Through Weight Factorization. CoRR, abs\/1902.01917","author":"Meller Eldad","year":"2019","unstructured":"Eldad Meller , Alexander Finkelstein , Uri Almog , and Mark Grobman . 2019. Same , Same But Different - Recovering Neural Network Quantization Error Through Weight Factorization. CoRR, abs\/1902.01917 ( 2019 ), arxiv:1902.01917 Eldad Meller, Alexander Finkelstein, Uri Almog, and Mark Grobman. 2019. Same, Same But Different - Recovering Neural Network Quantization Error Through Weight Factorization. CoRR, abs\/1902.01917 (2019), arxiv:1902.01917"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/581630.581674"},{"key":"e_1_3_2_1_65_1","volume-title":"Convolutional Neural Networks using Logarithmic Data Representation. CoRR, abs\/1603.01025","author":"Miyashita Daisuke","year":"2016","unstructured":"Daisuke Miyashita , Edward H. Lee , and Boris Murmann . 2016. Convolutional Neural Networks using Logarithmic Data Representation. CoRR, abs\/1603.01025 ( 2016 ), arxiv:1603.01025 Daisuke Miyashita, Edward H. Lee, and Boris Murmann. 2016. Convolutional Neural Networks using Logarithmic Data Representation. CoRR, abs\/1603.01025 (2016), arxiv:1603.01025"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.2196\/16194"},{"key":"e_1_3_2_1_67_1","volume-title":"Data-Free Quantization Through Weight Equalization and Bias Correction. In 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019","author":"Nagel Markus","year":"2019","unstructured":"Markus Nagel , Mart van Baalen , Tijmen Blankevoort , and Max Welling . 2019 . Data-Free Quantization Through Weight Equalization and Bias Correction. In 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019 , Seoul, Korea (South), October 27 - November 2, 2019. IEEE, 1325\u20131334. https:\/\/openaccess.thecvf.com\/content_ICCV_2019\/papers\/Nagel_Data-Free_Quantization_Through_Weight_Equalization_and_Bias_Correction_ICCV_2019_paper.pdf Markus Nagel, Mart van Baalen, Tijmen Blankevoort, and Max Welling. 2019. Data-Free Quantization Through Weight Equalization and Bias Correction. In 2019 IEEE\/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019. IEEE, 1325\u20131334. https:\/\/openaccess.thecvf.com\/content_ICCV_2019\/papers\/Nagel_Data-Free_Quantization_Through_Weight_Equalization_and_Bias_Correction_ICCV_2019_paper.pdf"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/DATE.2001.915108"},{"key":"e_1_3_2_1_69_1","volume-title":"Operating Systems: A Modern Perspective","author":"Nutt Gary J.","year":"2002","unstructured":"Gary J. Nutt . 2002 . Operating Systems: A Modern Perspective . Addison Wesley . isbn:9780201612431 lccn:2001033570 https:\/\/books.google.co.in\/books?id=AHBGAAAAYAAJ Gary J. Nutt. 2002. Operating Systems: A Modern Perspective. Addison Wesley. isbn:9780201612431 lccn:2001033570 https:\/\/books.google.co.in\/books?id=AHBGAAAAYAAJ"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/2737924.2737959"},{"key":"e_1_3_2_1_71_1","volume-title":"Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture. CoRR, abs\/1803.09256","author":"Peng Dezhi","year":"2018","unstructured":"Dezhi Peng , Zikai Sun , Zirong Chen , Zirui Cai , Lele Xie , and Lianwen Jin . 2018. Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture. CoRR, abs\/1803.09256 ( 2018 ), arXiv:1803.09256. arxiv:1803.09256 Dezhi Peng, Zikai Sun, Zirong Chen, Zirui Cai, Lele Xie, and Lianwen Jin. 2018. Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture. CoRR, abs\/1803.09256 (2018), arXiv:1803.09256. arxiv:1803.09256"},{"key":"e_1_3_2_1_72_1","volume-title":"XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. CoRR, abs\/1603.05279","author":"Rastegari Mohammad","year":"2016","unstructured":"Mohammad Rastegari , Vicente Ordonez , Joseph Redmon , and Ali Farhadi . 2016. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. CoRR, abs\/1603.05279 ( 2016 ), arxiv:1603.05279. arxiv:1603.05279 Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. 2016. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. CoRR, abs\/1603.05279 (2016), arxiv:1603.05279. arxiv:1603.05279"},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/2503210.2503296"},{"key":"e_1_3_2_1_74_1","volume-title":"Manik Varma, and Prateek Jain.","author":"Saha Oindrila","year":"2020","unstructured":"Oindrila Saha , Aditya Kusupati , Harsha Vardhan Simhadri , Manik Varma, and Prateek Jain. 2020 . RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. In Advances in Neural Information Processing Systems . Oindrila Saha, Aditya Kusupati, Harsha Vardhan Simhadri, Manik Varma, and Prateek Jain. 2020. RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_75_1","volume-title":"Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rkxaNjA9Ym","author":"Sakr Charbel","year":"2019","unstructured":"Charbel Sakr and Naresh Shanbhag . 2019 . Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rkxaNjA9Ym Charbel Sakr and Naresh Shanbhag. 2019. Per-Tensor Fixed-Point Quantization of the Back-Propagation Algorithm. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=rkxaNjA9Ym"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/2666356.2594302"},{"key":"e_1_3_2_1_78_1","volume-title":"The 2014 International Conference on Embedded Systems and Applications.","author":"Sharma Abhishek A.","year":"2014","unstructured":"Abhishek A. Sharma . 2014 . A Consolidated Review on Embedded MicroControllers for Pace Maker Applications . In The 2014 International Conference on Embedded Systems and Applications. Abhishek A. Sharma. 2014. A Consolidated Review on Embedded MicroControllers for Pace Maker Applications. In The 2014 International Conference on Embedded Systems and Applications."},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00251"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/2025113.2025133"},{"key":"e_1_3_2_1_81_1","volume-title":"International Conference on Machine Learning. 6105\u20136114","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le . 2019 . Efficientnet: Rethinking model scaling for convolutional neural networks . In International Conference on Machine Learning. 6105\u20136114 . Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. 6105\u20136114."},{"key":"e_1_3_2_1_82_1","unstructured":"ONNX Development Team. 2021. ONNX Model Zoo. https:\/\/github.com\/onnx\/models\/ \t\t\t\t  ONNX Development Team. 2021. ONNX Model Zoo. https:\/\/github.com\/onnx\/models\/"},{"key":"e_1_3_2_1_83_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000046589.39864.ee"},{"key":"e_1_3_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00881"},{"key":"e_1_3_2_1_85_1","volume-title":"Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition. arxiv:1804.03209.","author":"Warden Pete","year":"2018","unstructured":"Pete Warden . 2018 . Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition. arxiv:1804.03209. Pete Warden. 2018. Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition. arxiv:1804.03209."},{"key":"e_1_3_2_1_86_1","volume-title":"Proc. International Conference on Signal Processing Applications and Technology 1997 (ICSPAT-97)","author":"Willems M.","year":"1997","unstructured":"M. Willems . 1997 . FRIDGE : Floating-point programming of fixed-point digital signal processors . Proc. International Conference on Signal Processing Applications and Technology 1997 (ICSPAT-97) , Sept., https:\/\/ci.nii.ac.jp\/naid\/100 18558547\/en\/ M. Willems. 1997. FRIDGE : Floating-point programming of fixed-point digital signal processors. Proc. International Conference on Signal Processing Applications and Technology 1997 (ICSPAT-97), Sept., https:\/\/ci.nii.ac.jp\/naid\/10018558547\/en\/"},{"key":"e_1_3_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459172"},{"key":"e_1_3_2_1_88_1","volume-title":"WIDER FACE: A Face Detection Benchmark. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Yang Shuo","year":"2016","unstructured":"Shuo Yang , Ping Luo , Chen Change Loy , and Xiaoou Tang . 2016 . WIDER FACE: A Face Detection Benchmark. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Shuo Yang, Ping Luo, Chen Change Loy, and Xiaoou Tang. 2016. WIDER FACE: A Face Detection Benchmark. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_1_89_1","volume-title":"EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse. CoRR, abs\/1906.06579","author":"Yoo Young Joon","year":"2019","unstructured":"Young Joon Yoo , Dongyoon Han , and Sangdoo Yun . 2019 . EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse. CoRR, abs\/1906.06579 (2019), arXiv:1906.06579. arxiv:1906.06579 Young Joon Yoo, Dongyoon Han, and Sangdoo Yun. 2019. EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse. CoRR, abs\/1906.06579 (2019), arXiv:1906.06579. arxiv:1906.06579"},{"key":"e_1_3_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1109\/BTAS.2017.8272675"},{"key":"e_1_3_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.3390\/s19092158"},{"key":"e_1_3_2_1_92_1","volume-title":"Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc","author":"Zhao Yiren","unstructured":"Yiren Zhao , Xitong Gao , Daniel Bates , Robert Mullins , and Cheng-Zhong Xu. 2019. Focused Quantization for Sparse CNNs . In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc , E. Fox, and R. Garnett (Eds.). Curran Associates, Inc. , 5584\u20135593. http:\/\/papers.nips.cc\/paper\/8796-focused-quantization-for-sparse-cnns.pdf Yiren Zhao, Xitong Gao, Daniel Bates, Robert Mullins, and Cheng-Zhong Xu. 2019. Focused Quantization for Sparse CNNs. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alch\u00e9-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 5584\u20135593. http:\/\/papers.nips.cc\/paper\/8796-focused-quantization-for-sparse-cnns.pdf"},{"key":"e_1_3_2_1_93_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Zhou Aojun","year":"2017","unstructured":"Aojun Zhou , Anbang Yao , Yiwen Guo , Lin Xu , and Yurong Chen . 2017. Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights . In 5th International Conference on Learning Representations, ICLR 2017 , Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview .net. arxiv:1702.03044v2 Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, and Yurong Chen. 2017. Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. arxiv:1702.03044v2"},{"key":"e_1_3_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00982"},{"key":"e_1_3_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/2103621.2103710"}],"event":{"name":"LCTES '23: 24th ACM SIGPLAN\/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems","location":"Orlando FL USA","acronym":"LCTES '23","sponsor":["SIGBED ACM Special Interest Group on Embedded Systems","SIGPLAN ACM Special Interest Group on Programming Languages"]},"container-title":["Proceedings of the 24th ACM SIGPLAN\/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589610.3596278","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589610.3596278","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:45Z","timestamp":1750291425000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589610.3596278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,13]]},"references-count":95,"alternative-id":["10.1145\/3589610.3596278","10.1145\/3589610"],"URL":"https:\/\/doi.org\/10.1145\/3589610.3596278","relation":{},"subject":[],"published":{"date-parts":[[2023,6,13]]},"assertion":[{"value":"2023-06-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}