{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T21:41:20Z","timestamp":1782769280923,"version":"3.54.5"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100010661","name":"EC | Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["772369"],"award-info":[{"award-number":["772369"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"EC | Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["772369"],"award-info":[{"award-number":["772369"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"EC | Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["772369"],"award-info":[{"award-number":["772369"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"EC | Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["772369"],"award-info":[{"award-number":["772369"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"DOI":"10.1038\/s42256-021-00356-5","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T16:04:08Z","timestamp":1624291448000},"page":"675-686","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":184,"title":["Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors"],"prefix":"10.1038","volume":"3","author":[{"suffix":"Jr","given":"Claudionor N.","family":"Coelho","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aki","family":"Kuusela","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0055-2935","authenticated-orcid":false,"given":"Jennifer","family":"Ngadiuba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7671-243X","authenticated-orcid":false,"given":"Thea Klaeboe","family":"Aarrestad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladimir","family":"Loncar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maurizio","family":"Pierini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9034-0230","authenticated-orcid":false,"given":"Adrian Alan","family":"Pol","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sioni","family":"Summers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"356_CR1","doi-asserted-by":"crossref","unstructured":"Lin, S.-C. et al. The architectural implications of autonomous driving: constraints and acceleration. ACM SIGPLAN Notices 53, 751\u2013766 (2018).","DOI":"10.1145\/3296957.3173191"},{"key":"356_CR2","doi-asserted-by":"publisher","unstructured":"Ignatov, A. et al. AI benchmark: running deep neural networks on Android smartphones. In Computer Vision \u2013 ECCV 2018 Workshops. ECCV 2018 Lecture Notes in Computer Science Vol. 11133 (eds Leal-Taix\u00e9, L. & Roth, S.) 288\u2013314 (Springer, 2018); https:\/\/doi.org\/10.1007\/978-3-030-11021-5_19","DOI":"10.1007\/978-3-030-11021-5_19"},{"key":"356_CR3","doi-asserted-by":"crossref","unstructured":"Leber, C., Geib, B. & Litz, H. High frequency trading acceleration using FPGAs. In 2011 21st International Conference on Field Programmable Logic and Applications 317\u2013322 (IEEE, 2011).","DOI":"10.1109\/FPL.2011.64"},{"key":"356_CR4","unstructured":"The LHC Study Group. The Large Hadron Collider, Conceptual Design. Technical Report CERN\/AC\/95-05 (CERN, 1995)."},{"key":"356_CR5","doi-asserted-by":"crossref","unstructured":"Apollinari, G., B\u00e9jar Alonso, I., Br\u00fcning, O., Lamont, M. & Rossi, L. High-Luminosity Large Hadron Collider (HL-LHC): Preliminary Design Report. Technical Report (Fermi National Accelerator Laboratory, 2015).","DOI":"10.2172\/1365580"},{"key":"356_CR6","unstructured":"Iandola, F. N. et al. SqueezeNet: AlexNet-level accuracy with 50\u00d7 fewer parameters and <0.5-MB model size. Preprint at https:\/\/arxiv.org\/pdf\/1602.07360.pdf (2016)."},{"key":"356_CR7","unstructured":"Howard, A. G. et al. MobileNets: efficient convolutional neural networks for mobile vision applications. Preprint at https:\/\/arxiv.org\/pdf\/1704.04861.pdf (2017)."},{"key":"356_CR8","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.-C. MobileNetV2: inverted residuals and linear bottlenecks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 4510\u20134520 (IEEE, 2018).","DOI":"10.1109\/CVPR.2018.00474"},{"key":"356_CR9","doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T. & Sun, J. ShuffleNet V2: practical guidelines for efficient CNN architecture design. In Proc. European Conference on Computer Vision (ECCV) Lecture Notes in Computer Science 116\u2013131 (Springer, 2018).","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"356_CR10","doi-asserted-by":"crossref","unstructured":"Howard, A. et al. Searching for MobileNetV3. In Proc. IEEE International Conference on Computer Vision 1314\u20131324 (IEEE, 2019).","DOI":"10.1109\/ICCV.2019.00140"},{"key":"356_CR11","unstructured":"Ding, X. et al. Global sparse momentum SGD for pruning very deep neural networks. In Advances in Neural Information Processing Systems (eds Wallach, H. et al.) 6382\u20136394 (NIPS, 2019)."},{"key":"356_CR12","doi-asserted-by":"crossref","unstructured":"He, Y., Zhang, X. & Sun, J. Channel pruning for accelerating very deep neural networks. In Proc. IEEE International Conference on Computer Vision 1389\u20131397 (IEEE, 2017).","DOI":"10.1109\/ICCV.2017.155"},{"key":"356_CR13","doi-asserted-by":"publisher","first-page":"P07027","DOI":"10.1088\/1748-0221\/13\/07\/P07027","volume":"13","author":"J Duarte","year":"2018","unstructured":"Duarte, J. et al. Fast inference of deep neural networks in FPGAs for particle physics. J. Instrum. 13, P07027 (2018).","journal-title":"J. Instrum."},{"key":"356_CR14","doi-asserted-by":"crossref","unstructured":"Nagel, M., van Baalen, M., Blankevoort, T. & Welling, M. Data-free quantization through weight equalization and bias correction. In Proc. IEEE International Conference on Computer Vision 1325\u20131334 (IEEE, 2019).","DOI":"10.1109\/ICCV.2019.00141"},{"key":"356_CR15","unstructured":"Meller, E., Finkelstein, A., Almog, U. & Grobman, M. Same, same but different: recovering neural network quantization error through weight factorization. In Proc. 36th International Conference on Machine Learning (eds Chaudhuri, K. & Salakhutdinov, R.) 4486\u20134495 (PMLR, 2019)."},{"key":"356_CR16","unstructured":"Zhao, R., Hu, Y., Dotzel, J., De Sa, C. & Zhang, Z. Improving neural network quantization without retraining using outlier channel splitting. Preprint at https:\/\/arxiv.org\/pdf\/1901.09504.pdf (2019)."},{"key":"356_CR17","unstructured":"Banner, R., Nahshan, Y. & Soudry, D. Post training 4-bit quantization of convolutional networks for rapid-deployment. In Advances in Neural Information Processing Systems (eds Wallach, H. et al.) 7950\u20137958 (NIPS, 2019)."},{"key":"356_CR18","doi-asserted-by":"crossref","unstructured":"Moons, B., Goetschalckx, K., Van Berckelaer, N. & Verhelst, M. Minimum energy quantized neural networks. In 51st Asilomar Conference on Signals, Systems, and Computers 1921\u2013192 (ACSSC, 2017).","DOI":"10.1109\/ACSSC.2017.8335699"},{"key":"356_CR19","unstructured":"Courbariaux, M., Bengio, Y. & David, J.-P. BinaryConnect: training deep neural networks with binary weights during propagations. In Advances in Neural Information Processing Systems 28 (eds Cortes, C. et al.) 3123\u20133131 (Curran Associates, 2015)."},{"key":"356_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yang, J., Ye, D. & Hua, G. LQ-Nets: learned quantization for highly accurate and compact deep neural networks. In Proc. European Conference on Computer Vision (ECCV) 365\u2013382 (Springer, 2018).","DOI":"10.1007\/978-3-030-01237-3_23"},{"key":"356_CR21","unstructured":"Li, F. & Liu, B. Ternary weight networks. Preprint at https:\/\/arxiv.org\/pdf\/1605.04711.pdf (2016)."},{"key":"356_CR22","unstructured":"Zhou, S. et al. DoReFa-Net: training low bitwidth convolutional neural networks with low bitwidth gradients. Preprint at https:\/\/arxiv.org\/pdf\/1606.06160.pdf (2016)."},{"key":"356_CR23","first-page":"6869","volume":"18","author":"I Hubara","year":"2017","unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R. & Bengio, Y. Quantized neural networks: training neural networks with low precision weights and activations. J. Mach. Learn. Res. 18, 6869\u20136898 (2017).","journal-title":"J. Mach. Learn. Res."},{"key":"356_CR24","doi-asserted-by":"crossref","unstructured":"Rastegari, M., Ordonez, V., Redmon, J. & Farhadi, A. XNOR-Net: ImageNet classification using binary convolutional neural networks. In Computer Vision \u2013 ECCV 2016. Lecture Notes in Computer Science Vol. 9908 (eds Leibe, B. et al.) 525\u2013542 (Springer, 2016).","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"356_CR25","unstructured":"Micikevicius, P. et al. Mixed precision training. In International Conference on Learning Representations (ICLR, 2018)."},{"key":"356_CR26","doi-asserted-by":"crossref","unstructured":"Zhuang, B., Shen, C., Tan, M., Liu, L. & Reid, I. Towards effective low-bitwidth convolutional neural networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 7920\u20137928 (IEEE, 2018).","DOI":"10.1109\/CVPR.2018.00826"},{"key":"356_CR27","unstructured":"Wang, N., Choi, J., Brand, D., Chen, C.-Y. & Gopalakrishnan, K. Training deep neural networks with 8-bit floating point numbers. In Advances in Neural Information Processing Systems 7675\u20137684 (NIPS, 2018)."},{"key":"356_CR28","doi-asserted-by":"crossref","unstructured":"Wang, K., Liu, Z., Lin, Y., Lin, J. & Han, S. HAQ: hardware-aware automated quantization with mixed precision. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2019).","DOI":"10.1109\/CVPR.2019.00881"},{"key":"356_CR29","doi-asserted-by":"crossref","unstructured":"Dong, Z., Yao, Z., Gholami, A., Mahoney, M. & Keutzer, K. HAWQ: Hessian AWare Quantization of neural networks with mixed-precision. In Proc. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) 293\u2013302 (IEEE, 2019).","DOI":"10.1109\/ICCV.2019.00038"},{"key":"356_CR30","unstructured":"Dong, Z. et al. HAWQ-V2: Hessian AWare trace-weighted Quantization of neural networks. In Advances in Neural Information Processing Systems Vol. 33 (eds Larochelle, H. et al.) 18518\u201318529 (Curran Associates, 2020)."},{"key":"356_CR31","unstructured":"Wu, B. et al. Mixed precision quantization of ConvNets via differentiable neural architecture search. Preprint at https:\/\/arxiv.org\/pdf\/1812.00090.pdf (2018)."},{"key":"356_CR32","unstructured":"Chollet, F. et al. Keras https:\/\/github.com\/fchollet\/keras (2015)."},{"key":"356_CR33","unstructured":"Abadi, M. et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems http:\/\/tensorflow.org\/ (2015)."},{"key":"356_CR34","unstructured":"Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) 8024 (Curran Associates, 2019); https:\/\/arxiv.org\/pdf\/1912.01703.pdf"},{"key":"356_CR35","unstructured":"Open Neural Network Exchange Collaboration https:\/\/onnx.ai\/ (2017)."},{"key":"356_CR36","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/3186332","volume":"51","author":"SI Venieris","year":"2018","unstructured":"Venieris, S. I., Kouris, A. & Bouganis, C.-S. Toolflows for mapping convolutional neural networks on FPGAs: a survey and future directions. ACM Comput. Surv. 51, 56 (2018).","journal-title":"ACM Comput. Surv."},{"key":"356_CR37","first-page":"2","volume":"12","author":"K Guo","year":"2018","unstructured":"Guo, K., Zeng, S., Yu, J., Wang, Y. & Yang, H. A survey of FPGA-based neural network inference accelerators. ACM Trans. Reconfigurable Technol. Syst. 12, 2 (2018).","journal-title":"ACM Trans. Reconfigurable Technol. Syst."},{"key":"356_CR38","doi-asserted-by":"publisher","first-page":"7823","DOI":"10.1109\/ACCESS.2018.2890150","volume":"7","author":"A Shawahna","year":"2019","unstructured":"Shawahna, A., Sait, S. M. & El-Maleh, A. FPGA-based accelerators of deep learning networks for learning and classification: a review. IEEE Access 7, 7823\u20137859 (2019).","journal-title":"IEEE Access"},{"key":"356_CR39","unstructured":"Abdelouahab, K., Pelcat, M., Serot, J. & Berry, F. Accelerating CNN inference on FPGAs: a survey. Preprint at https:\/\/arxiv.org\/pdf\/1806.01683.pdf (2018)."},{"key":"356_CR40","unstructured":"Intel. Intel High Level Synthesis Compiler https:\/\/www.intel.com\/content\/www\/us\/en\/software\/programmable\/quartus-prime\/hls-compiler.html (2020)."},{"key":"356_CR41","unstructured":"Mentor\/Siemens. Catapult High-Level Synthesis https:\/\/www.mentor.com\/hls-lp\/catapult-high-level-synthesis (2020)."},{"key":"356_CR42","doi-asserted-by":"publisher","first-page":"598927","DOI":"10.3389\/fdata.2020.598927","volume":"3","author":"Y Iiyama","year":"2021","unstructured":"Iiyama, Y. et al. Distance-weighted graph neural networks on FPGAs for real-time particle reconstruction in high energy physics. Front. Big Data 3, 598927 (2021).","journal-title":"Front. Big Data"},{"key":"356_CR43","doi-asserted-by":"publisher","first-page":"015001","DOI":"10.1088\/2632-2153\/aba042","volume":"2","author":"J Ngadiuba","year":"2020","unstructured":"Ngadiuba, J. et al. Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml. Mach. Learn. Sci. Technol. 2, 015001 (2020).","journal-title":"Mach. Learn. Sci. Technol."},{"key":"356_CR44","doi-asserted-by":"crossref","unstructured":"Umuroglu, Y. et al. FINN: a framework for fast, scalable binarized neural network inference. In Proc. 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays 65\u201374 (ACM, 2017).","DOI":"10.1145\/3020078.3021744"},{"key":"356_CR45","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1145\/3242897","volume":"11","author":"M Blott","year":"2018","unstructured":"Blott, M. et al. FINN-R: an end-to-end deep-learning framework for fast exploration of quantized neural networks. ACM Trans. Reconfigurable Technol. Syst. 11, 16 (2018).","journal-title":"ACM Trans. Reconfigurable Technol. Syst."},{"key":"356_CR46","doi-asserted-by":"publisher","unstructured":"Alessandro, F. G. & Nickfraser, U. Y. Xilinx\/brevitas: Release version 0.2.1 https:\/\/doi.org\/10.5281\/zenodo.4507794 (2021).","DOI":"10.5281\/zenodo.4507794"},{"key":"356_CR47","doi-asserted-by":"crossref","unstructured":"Umuroglu, Y., Akhauri, Y., Fraser, N. J. & Blott, M. LogicNets: co-designed neural networks and circuits for extreme-throughput applications. In 30th International Conference on Field-Programmable Logic and Applications 291\u2013297 (IEEE, 2020).","DOI":"10.1109\/FPL50879.2020.00055"},{"key":"356_CR48","doi-asserted-by":"crossref","unstructured":"Guan, Y. et al. FP-DNN: an automated framework for mapping deep neural networks onto FPGAs with RTL-HLS hybrid templates. In 2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) 152 (IEEE, 2017).","DOI":"10.1109\/FCCM.2017.25"},{"key":"356_CR49","doi-asserted-by":"publisher","unstructured":"Sharma, H. et al. From high-level deep neural models to FPGAs. In Proc. 2016 49th Annual IEEE\/ACM International Symposium on Microarchitecture 1 (IEEE, 2016); https:\/\/doi.org\/10.1109\/MICRO.2016.7783720","DOI":"10.1109\/MICRO.2016.7783720"},{"key":"356_CR50","doi-asserted-by":"crossref","unstructured":"Gokhale, V., Zaidy, A., Chang, A. X. M. & Culurciello, E. Snowflake: an efficient hardware accelerator for convolutional neural networks. In Proc. 2017 IEEE International Symposium on Circuits and Systems (ISCAS) 1\u20134 (IEEE, 2017).","DOI":"10.1109\/ISCAS.2017.8050809"},{"key":"356_CR51","doi-asserted-by":"crossref","unstructured":"Venieris, S. I. & Bouganis, C.-S. fpgaConvNet: a toolflow for mapping diverse convolutional neural networks on embedded FPGAs. In Proc. NIPS 2017 Workshop on Machine Learning on the Phone and other Consumer Devices (NIPS, 2017); https:\/\/arxiv.org\/pdf\/1711.08740.pdf","DOI":"10.1145\/3020078.3021791"},{"key":"356_CR52","doi-asserted-by":"crossref","unstructured":"Venieris, S. I. & Bouganis, C.-S. fpgaConvNet: automated mapping of convolutional neural networks on FPGAs. In Proc. 2017 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays 291 (ACM, 2017).","DOI":"10.1145\/3020078.3021791"},{"key":"356_CR53","doi-asserted-by":"crossref","unstructured":"Venieris, S. I. & Bouganis, C.-S. fpgaConvNet: a framework for mapping convolutional neural networks on FPGAs. In Proc. 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) 40 (IEEE, 2016).","DOI":"10.1109\/FCCM.2016.22"},{"key":"356_CR54","doi-asserted-by":"crossref","unstructured":"Huimin Li et al. A high performance FPGA-based accelerator for large-scale convolutional neural networks. In Proc. 2016 26th International Conference on Field Programmable Logic and Applications (FPL) 1\u20139 (IEEE, 2016).","DOI":"10.1109\/FPL.2016.7577308"},{"key":"356_CR55","doi-asserted-by":"crossref","unstructured":"Zhao, R. et al. Hardware compilation of deep neural networks: an overview. In 2018 IEEE 29th International Conference on Application-Specific Systems, Architectures and Processors (ASAP) 1\u20138 (IEEE, 2018).","DOI":"10.1109\/ASAP.2018.8445088"},{"key":"356_CR56","unstructured":"Google. TensorFlow Lite https:\/\/www.tensorflow.org\/lite (2020)."},{"key":"356_CR57","doi-asserted-by":"publisher","DOI":"10.1140\/epjc\/s10052-020-7608-4","volume":"80","author":"EA Moreno","year":"2019","unstructured":"Moreno, E. A. et al. JEDI-net: a jet identification algorithm based on interaction networks. Eur. Phys. J. C 80, 58 (2019).","journal-title":"Eur. Phys. J. C"},{"key":"356_CR58","doi-asserted-by":"publisher","unstructured":"Pierini, M., Duarte, J. M., Tran, N. & Freytsis, M. HLS4ML LHC Jet Dataset (150 particles) https:\/\/doi.org\/10.5281\/zenodo.3602260 (2020).","DOI":"10.5281\/zenodo.3602260"},{"key":"356_CR59","unstructured":"Zhu, M. & Gupta, S. To prune, or not to prune: exploring the efficacy of pruning for model compression. Preprint at https:\/\/arxiv.org\/pdf\/1710.01878.pdf (2017)."},{"key":"356_CR60","unstructured":"Coelho, C. Qkeras https:\/\/github.com\/google\/qkeras (2019)."},{"key":"356_CR61","unstructured":"Nair, V. & Hinton, G. E. Rectified linear units improve restricted boltzmann machines. In Proc. 27th International Conference on International Conference on Machine Learning 807\u2013814 (ICML, 2010)."},{"key":"356_CR62","unstructured":"Hennessy, J. L. & et al. Computer Architecture: a Quantitative Approach 6th edn (Morgan Kaufmann, 2016)."},{"key":"356_CR63","doi-asserted-by":"crossref","unstructured":"Horowitz, M. Computing\u2019s energy problem (and what we can do about it). In Proc. 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC) 10\u201314 (IEEE, 2014).","DOI":"10.1109\/ISSCC.2014.6757323"},{"key":"356_CR64","unstructured":"O\u2019Malley, T. et al. Keras Tuner https:\/\/github.com\/keras-team\/keras-tuner (2019)."},{"key":"356_CR65","first-page":"6765","volume":"18","author":"L Li","year":"2017","unstructured":"Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. & Talwalkar, A. Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18, 6765\u20136816 (2017).","journal-title":"J. Mach. Learn. Res."},{"key":"356_CR66","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770\u2013778 (IEEE, 2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"356_CR67","unstructured":"Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016)."},{"key":"356_CR68","unstructured":"Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In 3rd International Conference on Learning Representations (ICLR) 2015, Conference Track Proceedings (eds Bengio, Y. & LeCun, Y.) (ICLR, 2015); https:\/\/arxiv.org\/pdf\/1412.6980.pdf"},{"key":"356_CR69","doi-asserted-by":"crossref","unstructured":"Aarrestad, T. et al. Fast convolutional neural networks on FPGAs with hls4ml. Preprint at https:\/\/arxiv.org\/pdf\/2101.05108.pdf (2021).","DOI":"10.1088\/2632-2153\/ac0ea1"},{"key":"356_CR70","unstructured":"Netzer, Y. et al. Reading digits in natural images with unsupervised feature learning. In Proc. NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning (NIPS, 2011); https:\/\/deeplearningworkshopnips2011.files.wordpress.com\/2011\/12\/12.pdf"},{"key":"356_CR71","unstructured":"Gupta, S., Agrawal, A., Gopalakrishnan, K. & Narayanan, P. Deep learning with limited numerical precision. In Proc. 32nd International Conference on Machine Learning 1737\u20131746 (PMLR, 2015)."},{"key":"356_CR72","unstructured":"Kwan, H. K. & Tang, C. Z. A design method for multilayer feedforward neural networks for simple hardware implementation. In Proc. 1993 IEEE International Symposium on Circuits and Systems Vol. 4, 2363\u20132366 (IEEE, 1993)."},{"key":"356_CR73","unstructured":"Howard, A. G. et al. MobileNets: efficient convolutional neural networks for mobile vision applications. Preprint at https:\/\/arxiv.org\/pdf\/1704.04861.pdf (2017)."},{"key":"356_CR74","unstructured":"Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (eds Bengio, Y. & LeCun, Y.) (ICLR, 2015); https:\/\/arxiv.org\/pdf\/1409.1556.pdf"},{"key":"356_CR75","unstructured":"Das, D. et al. Mixed precision training of convolutional neural networks using integer operations. In International Conference on Learning Representations (ICLR, 2018)."},{"key":"356_CR76","doi-asserted-by":"crossref","unstructured":"Hwang, K. & Sung, W. Fixed-point feedforward deep neural network design using weights +1, 0, and \u22121. In Proc. 2014 IEEE Workshop on Signal Processing Systems (SiPS) 1\u20136 (IEEE, 2014).","DOI":"10.1109\/SiPS.2014.6986082"},{"key":"356_CR77","unstructured":"Li, F., Zhang, B. & Liu, B. Ternary weight networks. Preprint at https:\/\/arxiv.org\/pdf\/1605.04711.pdf (2016)."}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00356-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00356-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00356-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T20:34:37Z","timestamp":1670099677000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00356-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":77,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["356"],"URL":"https:\/\/doi.org\/10.1038\/s42256-021-00356-5","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,21]]},"assertion":[{"value":"23 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}