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In this paper, we show how to use oscillators to process time-dependent waveforms with minimal or no preprocessing. Since preprocessing and first-layer processing are often the most power-hungry steps in neural networks, our findings may open new doors to simple and power-efficient edge-AI devices.<\/jats:p>","DOI":"10.3390\/info16070611","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T15:48:22Z","timestamp":1752680902000},"page":"611","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Oscillator-Based Processing Unit for Formant Recognition"],"prefix":"10.3390","volume":"16","author":[{"given":"Tam\u00e1s","family":"Rudner-Hal\u00e1sz","sequence":"first","affiliation":[{"name":"Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, 1083 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wolfgang","family":"Porod","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Notre Dame (NDnano), Notre Dame, IN 46556, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7592-0256","authenticated-orcid":false,"given":"Gyorgy","family":"Csaba","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology and Bionics, P\u00e1zm\u00e1ny P\u00e9ter Catholic University, 1083 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"011302","DOI":"10.1063\/1.5120412","article-title":"Coupled oscillators for computing: A review and perspective","volume":"7","author":"Csaba","year":"2020","journal-title":"Appl. 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Proceedings of the 2020 XXXV Conference on Design of Circuits and Integrated Systems (DCIS), Segovia, Spain.","DOI":"10.1109\/DCIS51330.2020.9268618"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Rudner-Hal\u00e1sz, T., Porod, W., and Csaba, G. (2024). Design of oscillatory neural networks by machine learning. Front. Neurosci., 18.","DOI":"10.3389\/fnins.2024.1307525"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Scellier, B., and Bengio, Y. (2017). Equilibrium propagation: Bridging the gap between energy-based models and backpropagation. Front. Comput. Neurosci., 11.","DOI":"10.3389\/fncom.2017.00024"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3671","DOI":"10.1038\/s41467-024-46879-4","article-title":"Training an ising machine with equilibrium propagation","volume":"15","author":"Laydevant","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"034014","DOI":"10.1088\/2634-4386\/ad752b","article-title":"Training coupled phase oscillators as a neuromorphic platform using equilibrium propagation","volume":"4","author":"Wang","year":"2024","journal-title":"Neuromorph. Comput. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1007\/s13369-022-07086-9","article-title":"Accent recognition using a spectrogram image feature-based convolutional neural network","volume":"48","author":"Cetin","year":"2023","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1038\/s41586-018-0632-y","article-title":"Vowel recognition with four coupled spin-torque nano-oscillators","volume":"563","author":"Romera","year":"2018","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1038\/s41467-020-20692-1","article-title":"Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing","volume":"12","author":"Zhong","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1109\/JXCDC.2015.2504049","article-title":"Coupled-oscillator associative memory array operation for pattern recognition","volume":"1","author":"Nikonov","year":"2015","journal-title":"IEEE J. Explor. Solid-State Comput. Devices Circ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/TNN.2010.2086476","article-title":"Learning Pattern Recognition Through Quasi-Synchronization of Phase Oscillators","volume":"22","author":"Vassilieva","year":"2011","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1162\/089976698300017511","article-title":"Spike Frequency Adaptation Affects the Synchronization Properties of Networks of Cortical Oscillators","volume":"10","author":"Crook","year":"1998","journal-title":"Neural Comput."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Taylor, D., Ott, E., and Restrepo, J.G. (2010). Spontaneous synchronization of coupled oscillator systems with frequency adaptation. Phys. Rev. E, 81.","DOI":"10.1103\/PhysRevE.81.046214"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Csaba, G., Ytterdal, T., and Porod, W. (2016, January 11\u201314). Neural network based on parametrically-pumped oscillators. Proceedings of the 2016 IEEE International Conference on Electronics, Circuits and Systems (ICECS), Monte Carlo, Monaco.","DOI":"10.1109\/ICECS.2016.7841128"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pikovsky, A., Rosenblum, M., and Kurths, J. (2001). Synchronization: A Universal Concept in Nonlinear Sciences, Cambridge University Press.","DOI":"10.1017\/CBO9780511755743"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hughes, T.W., Williamson, I.A., Minkov, M., and Fan, S. (2019). Wave physics as an analog recurrent neural network. Sci. Adv., 5.","DOI":"10.1126\/sciadv.aay6946"},{"key":"ref_22","unstructured":"Chen, R.T.Q. (2025, May 01). torchdiffeq GitHub Repository. Available online: https:\/\/github.com\/rtqichen\/torchdiffeq."},{"key":"ref_23","unstructured":"Rapin, J., and Teytaud, O. (2025, May 01). Nevergrad-A Gradient-Free Optimization Platform. GitHub Repository. Available online: https:\/\/GitHub.com\/FacebookResearch\/Nevergrad."},{"key":"ref_24","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in pytorch. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ballo, A., Pennisi, S., Scotti, G., and Venezia, C. (2022). A 0.5 V sub-threshold CMOS current-controlled ring oscillator for IoT and implantable devices. J. Low Power Electron. Appl., 12.","DOI":"10.3390\/jlpea12010016"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tunheim, S.A., Zheng, Y., Jiao, L., Shafik, R., Yakovlev, A., and Granmo, O.C. (2025). An All-digital 65-nm Tsetlin Machine Image Classification Accelerator with 8.6 nJ per MNIST Frame at 60.3 k Frames per Second. arXiv.","DOI":"10.1109\/TCSI.2025.3586698"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/611\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:10:55Z","timestamp":1760033455000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/7\/611"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,16]]},"references-count":26,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["info16070611"],"URL":"https:\/\/doi.org\/10.3390\/info16070611","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2025,7,16]]}}}