{"id":48792,"date":"2025-10-01T10:10:08","date_gmt":"2025-10-01T10:10:08","guid":{"rendered":"https:\/\/www.newsbeep.com\/il\/48792\/"},"modified":"2025-10-01T10:10:08","modified_gmt":"2025-10-01T10:10:08","slug":"designing-nonlinearity-in-a-current-starved-ring-oscillator-for-reservoir-computing-hardware","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/il\/48792\/","title":{"rendered":"Designing nonlinearity in a current-starved ring oscillator for reservoir computing hardware"},"content":{"rendered":"

Jaeger, H. & Haas, H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304 (5667), 78\u201380. https:\/\/doi.org\/10.1126\/science.1091277<\/a> (2004).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pati, S. P. et al. Real-time information processing via volatile resistance change in scalable protonic devices. Commun. Mat. 5 (1). https:\/\/doi.org\/10.1038\/s43246-024-00621-1<\/a> (2024).<\/p>\n

Jaeger, H. The \u201cecho state\u201d approach to analysing and training recurrent neural networks (2010).<\/p>\n

Maass, W., Natschl\u00e4ger, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14 (11), 2531\u20132560. https:\/\/doi.org\/10.1162\/089976602760407955<\/a> (2002).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Abe, Y. et al. Highly-integrable analogue reservoir circuits based on a simple cycle architecture. Sci. Rep. 14 (1). https:\/\/doi.org\/10.1038\/s41598-024-61880-z<\/a> (2024).<\/p>\n

Moon, J. et al. Temporal data classification and forecasting using a memristor-based reservoir computing system. Nat. Electron. 2 (10), 480\u2013487. https:\/\/doi.org\/10.1038\/s41928-019-0313-3<\/a> (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nishioka, D. et al. Edge-of-chaos learning achieved by ion-electron\u2013coupled dynamics in an ion-gating reservoir. Sci. Adv. 8 (50). https:\/\/doi.org\/10.1126\/sciadv.ade1156<\/a> (2022).<\/p>\n

Hochstetter, J. et al. Avalanches and edge-of-chaos learning in neuromorphic nanowire networks. Nat. Commun. 12 (1). https:\/\/doi.org\/10.1038\/s41467-021-24260-z<\/a> (2021).<\/p>\n

Usami, Y. et al. In-materio reservoir computing in a sulfonated polyaniline network. Adv. Mat. 33(48). https:\/\/doi.org\/10.1002\/adma.202102688<\/a> (2021).<\/p>\n

Indiveri, G. Computation in neuromorphic analog VLSI systems. In Neural Nets WIRN Vietri-01. Persp. Neur. Comp. (eds. Tagliaferri, R., Marinaro, M.) 3\u201320. https:\/\/doi.org\/10.1007\/978-1-4471-0219-9_1<\/a> (2002).<\/p>\n

Nguyen, D. A., Tran, X. T. & Iacopi, F. A review of algorithms and hardware implementations for spiking neural networks. J. Low Power Electron. Appl. 11 (2), 23. https:\/\/doi.org\/10.3390\/jlpea11020023<\/a> (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Misra, J. & Saha, I. Artificial neural networks in hardware: a survey of two decades of progress. Neurocommun 74 (1\u20133), 239\u2013255. https:\/\/doi.org\/10.1016\/j.neucom.2010.03.021<\/a> (2010).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Auge, D., Hille, J., Mueller, E. & Knoll, A. A survey of encoding techniques for signal processing in spiking neural networks. Neur Proces Lett. 53 (6), 4693\u20134710. https:\/\/doi.org\/10.1007\/s11063-021-10562-2<\/a> (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Javanshir, A., Nguyen, T. T., Mahmud, M. P. & Kouzani, A. Z. Advancements in algorithms and neuromorphic hardware for spiking neural networks. Neural Comput. 34 (6), 1289\u20131328. https:\/\/doi.org\/10.1162\/neco_a_01499<\/a> (2022).<\/p>\n

Article<\/a>\u00a0
\n MathSciNet<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bouvier, M. et al. Spiking neural networks hardware implementations and challenges: A survey. ACM J. Emer. Tech. Comp. Syst. (JETC). 15 (2), 1\u201335. https:\/\/doi.org\/10.1145\/3304103<\/a> (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Maass, W. Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10 (9), 1659\u20131671. https:\/\/doi.org\/10.1016\/S0893-6080(97)00011-7<\/a> (1997).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gupta, A. & Long, L. N. Character recognition using spiking neural networks. IEEE Int. Conf. Neural Netw. 46, 53\u201358. https:\/\/doi.org\/10.1109\/ijcnn.2007.4370930<\/a> (2007).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Escobar, M., Masson, G. S., Vieville, T. & Kornprobst, P. Action recognition using a bio-inspired feedforward spiking network. Int. J. Comp. Vis. 82 (3), 284\u2013301. https:\/\/doi.org\/10.1007\/s11263-008-0201-1<\/a> (2009).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Meftah, B., Lezoray, O. & Benyettou, A. Segmentation and edge detection based on spiking neural network model. Neur Proc. Lett. 32 (2), 131\u2013146. https:\/\/doi.org\/10.1007\/s11063-010-9149-6<\/a> (2010).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zheng, H., Wu, Y., Deng, L., Hu, Y. & Li, G. Going deeper with directly-trained larger spiking neural networks. Proc. AAAI Conf. Artif. Intell. 35 (12), 11062\u201311070. https:\/\/doi.org\/10.1609\/aaai.v35i12.17320<\/a> (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tavanaei, A. & Maida, A. Bio-inspired multi-layer spiking neural network extracts discriminative features from speech signals. Lecture Notes Comp. Sci. 899\u2013908. https:\/\/doi.org\/10.1007\/978-3-319-70136-3_95<\/a> (2017).<\/p>\n

Loiselle, S., Rouat, J., Pressnitzer, D. & Thorpe, S. J. Exploration of rank order coding with spiking neural networks for speech recognition. Proce. 2005 IEEE Int. Joint Conf. Neural. Netw. (2005). https:\/\/doi.org\/10.1109\/ijcnn.2005.1556220<\/a> (2005).<\/p>\n

Ghosh-Dastidar, S. & Adeli, H. Improved spiking neural networks for EEG classification and epilepsy and seizure detection. Integ Comput.-Aided Eng. 14 (3), 187\u2013212. https:\/\/doi.org\/10.3233\/ica-2007-14301<\/a> (2007).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kasabov, N. et al. Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke. Neurocomput. 134, 269\u2013279. https:\/\/doi.org\/10.1016\/j.neucom.2013.09.049<\/a> (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Furber, S. B., Galluppi, F., Temple, S. & Plana, L. A. The spinnaker project. Proc. IEEE. 102 (5), 652\u2013665. https:\/\/doi.org\/10.1109\/JPROC.2014.2304638<\/a> (2014).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Akopyan, F. et al. TrueNorth: design and tool flow of a 65\u00a0MW 1 million neuron programmable neurosynaptic chip. IEEE Trans. Comput.-Aided Des. Integ Circ. Syst. 34 (10), 1537\u20131557. https:\/\/doi.org\/10.1109\/tcad.2015.2474396<\/a> (2015).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Benjamin, B. V. et al. Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102(5), 699\u2013716. https:\/\/doi.org\/10.1109\/jproc.2014.2313565<\/a> (2014).<\/p>\n

Davies, M. et al. Loihi: A neuromorphic Manycore processor with on-chip learning. IEEE Mic. 38 (1), 82\u201399. https:\/\/doi.org\/10.1109\/mm.2018.112130359<\/a> (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Thakur, C. S. et al. Large-scale neuromorphic spiking array processors: a quest to mimic the brain. Front. Neurosci. 12 https:\/\/doi.org\/10.3389\/fnins.2018.00891<\/a> (2018).<\/p>\n

Pehle, C. et al. The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticity. Front. Neurosci. 16 https:\/\/doi.org\/10.3389\/fnins.2022.795876<\/a> (2022).<\/p>\n

Mead, C. Neuromorphic electronic systems. Proc. IEEE. 78 (10), 1629\u20131636. https:\/\/doi.org\/10.1109\/5.58356<\/a> (1990).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Armbrust, M. et al. A view of cloud computing. Commun. ACM. 53 (4), 50\u201358. https:\/\/doi.org\/10.1145\/1721654.1721672<\/a> (2010).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Shi, W., Cao, J., Zhang, Q., Li, Y. & Xu, L. Edge computing: vision and challenges. IEEE Int. Thin J. 3 (5), 637\u2013646. https:\/\/doi.org\/10.1109\/jiot.2016.2579198<\/a> (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dowrick, T., McDaid, L. & Hall, S. Fan-in analysis of a leaky integrator circuit using charge transfer synapses. Neurocomput 314, 78\u201385. https:\/\/doi.org\/10.1016\/j.neucom.2018.06.065<\/a> (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mondal, A. J., Talukdar, J. & Bhattacharyya, B. K. Estimation of frequency and amplitude of ring oscillator built using current sources. Ain S Eng. J. 11 (3), 677\u2013686. https:\/\/doi.org\/10.1016\/j.asej.2020.01.006<\/a> (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Joubert, A., Belhadj, B., Temam, O. & H\u00e9liot, R. Hardware spiking neurons design: analog or digital? In The 2012 Int. Joint Conf. Neural. Netw. (IJCNN), 1\u20135. https:\/\/doi.org\/10.1109\/IJCNN.2012.6252600<\/a> (2012).<\/p>\n

Indiveri, G., Chicca, E. & Douglas, R. A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans. Neural Netw. 17 (1), 211\u2013221. https:\/\/doi.org\/10.1109\/tnn.2005.860850<\/a> (2006).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Chen, X. et al. CMOS-based area-and-power-efficient neuron and synapse circuits for time-domain analog spiking neural networks. Appl. Phys. Lett. 122 (7). https:\/\/doi.org\/10.1063\/5.0136627<\/a> (2023).<\/p>\n

Yajima, T. Ultra-low-power switching circuits based on a binary pattern generator with spiking neurons. Sci. Rep. 12 (1). https:\/\/doi.org\/10.1038\/s41598-022-04982-w<\/a> (2022).<\/p>\n

Wu, X., Saxena, V., Zhu, K. & Balagopal, S. A CMOS spiking neuron for brain-inspired neural networks with resistive synapses and in situ learning. IEEE Trans. Circuits Syst. II Expr Brie. 62 (11), 1088\u20131092. https:\/\/doi.org\/10.1109\/tcsii.2015.2456372<\/a> (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nugroho, P., Leelarasmee, E. & Fujii, N. Tuning analysis of a CMOS current controlled ring oscillator. In 21st Int. Tech. Conf. Circuits\/Systems Comput. Commun, 49\u201352 (2006).<\/p>\n

Feldman, A., Nechushtan, O. & Shor, J. Voltage level detection for near-Vth computing. IEEE J. Solid-State Circ. 59, 1847\u20131857. https:\/\/doi.org\/10.1109\/JSSC.2023.3309865<\/a> (2024).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wijekoon, J. H. B. & Dudek, P. Compact silicon neuron circuit with spiking and bursting behavior. Neural Netw. 21, 524\u2013534. https:\/\/doi.org\/10.1016\/j.neunet.2007.12.037<\/a> (2008).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Livi, P. & Indiveri, G. A current-mode conductance-based silicon neuron for address-event neuromorphic systems. In 2009 IEEE Int. Symp. Circ. and Syst., 2898\u20132901. https:\/\/doi.org\/10.1109\/iscas.2009.5118408<\/a> (2009).<\/p>\n

Sourikopoulos, I. et al. A 4-fJ\/spike artificial neuron in 65 Nm CMOS technology. Front. Neurosci. 11. https:\/\/doi.org\/10.3389\/fnins.2017.00123<\/a> (2017).<\/p>\n

Yue, K., Wang, X., Jadav, J., Vartak, A. & Parker, A. C. Analog neurons that signal with spiking frequencies. In Proc. Int. Conf. Neuromorphic Syst., 1\u20138. https:\/\/doi.org\/10.1145\/3354265.3354273<\/a> (2019). <\/p>\n

Zare, M., Zafarkhah, E. & Anzabi-Nezhad, N. S. An area and energy efficient LIF neuron model with spike frequency adaptation mechanism. Neurocomput 465, 350\u2013358. https:\/\/doi.org\/10.1016\/j.neucom.2021.09.004<\/a> (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Quan, J., Liu, Z., Li, B. & Luo, J. Ultra-low-power compact neuron circuit with tunable spiking frequency and high robustness in 22 nm FDSOI. Electron 12 (12), 2648. https:\/\/doi.org\/10.3390\/electronics12122648<\/a> (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Quan, J., Liu, Z., Li, B., Zeng, C. & Luo, J. 55 Nm CMOS mixed-signal neuromorphic circuits for constructing energy-efficient reconfigurable SNNs. Electron. 12 (19), 4147. https:\/\/doi.org\/10.3390\/electronics12194147<\/a> (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Siddique, R., Eftimie, M. & Banad, Y. M. A comparative analysis of neuromorphic neuron circuits for enhanced power efficiency and spiking frequency in 22nm CMOS technology. 2024 IEEE 67th Int. Midw. Sym. Circ. and Syst. (MWSCAS), 1096\u20131100. https:\/\/doi.org\/10.1109\/mwscas60917.2024.10658829<\/a> (2024).<\/p>\n

Singh, A., Kim, D., Lee, H. & Lee, B. G. A high-frequency CMOS meminductor emulator for spiking neuron. IEEE Trans. Circ. Syst. I Reg. Pap. 72 (3), 1056\u20131067. https:\/\/doi.org\/10.1109\/tcsi.2024.3454553<\/a> (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

MS, D., Raghu, H. R. S., SB, R. & J., & Simulation-based effective comparative analysis of neuron circuits for neuromorphic computation systems. Neurocomput. 614, 128758, 1\u201312. https:\/\/doi.org\/10.1016\/j.neucom.2024.128758<\/a> (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tanaka, G., Matsumori, T., Yoshida, H. & Aihara, K. Reservoir computing with diverse timescales for prediction of multiscale dynamics. Phys. Rev. Resea. 4 (3). https:\/\/doi.org\/10.1103\/physrevresearch.4.l032014<\/a> (2022).<\/p>\n

Lee, Y., Seong, T., Yoo, S. & Choi, J. A low-jitter and low-reference-spur ring-VCO-based switched-loop filter PLL using a fast phase-error correction technique. IEEE J. Solid-State Circ. 53 (4), 1192\u20131202. https:\/\/doi.org\/10.1109\/jssc.2017.2768411<\/a> (2018).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ho, Y., Yang, Y., Chang, C. & Su, C. A near-threshold 480\u00a0mhz 78 \u00b5W all-digital PLL with a bootstrapped DCO. IEEE J. Solid-State Circ. 48 (11), 2805\u20132814. https:\/\/doi.org\/10.1109\/jssc.2013.2280409<\/a> (2013).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, Y., Rhee, W., Kim, T., Park, H. & Wang, Z. A 0.35\u20130.5-V 18\u2013152\u00a0MHz digitally controlled relaxation oscillator with adaptive threshold calibration in 65-Nm CMOS. IEEE Trans. Circ. Syst. II Expr Brie. 62 (8), 736\u2013740. https:\/\/doi.org\/10.1109\/tcsii.2015.2433531<\/a> (2015).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhu, Z. & Feng, L. A review of sub-\u00b5W CMOS analog computing circuits for instant 1-dimensional audio signal processing in always-on edge devices. IEEE Trans. Circ. Syst. I Reg. Pap. 71 (9), 4009\u20134018. https:\/\/doi.org\/10.1109\/tcsi.2024.3408819<\/a> (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, H. & Wang, Y. A 5.27nW\/kHz low power relaxation oscillator with current consumption reduction technique. 2024 IEEE 7th Adv. Info. Tech., Electro. and Auto. Contr. Conf. (IAEAC), Chongqing, China., 1336\u20131339. https:\/\/doi.org\/10.1109\/IAEAC59436.2024.10503920<\/a> (2024).<\/p>\n

Tanaka, G. et al. Recent advances in physical reservoir computing: A review. Neural Netw. 115, 100\u2013123. https:\/\/doi.org\/10.1016\/j.neunet.2019.03.005<\/a> (2019).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n","protected":false},"excerpt":{"rendered":"Jaeger, H. & Haas, H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304…\n","protected":false},"author":2,"featured_media":48793,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21],"tags":[353,37864,37862,8763,19150,4068,85,46,2159,4069,37863,370,37865,141,125],"class_list":{"0":"post-48792","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-computing","8":"tag-computing","9":"tag-current-starved-ring-oscillator","10":"tag-energy-science-and-technology","11":"tag-engineering","12":"tag-frequency","13":"tag-humanities-and-social-sciences","14":"tag-il","15":"tag-israel","16":"tag-learning","17":"tag-multidisciplinary","18":"tag-nonlinear","19":"tag-physics","20":"tag-reservoir-computing","21":"tag-science","22":"tag-technology"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts\/48792","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/comments?post=48792"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/posts\/48792\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/media\/48793"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/media?parent=48792"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/categories?post=48792"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/il\/wp-json\/wp\/v2\/tags?post=48792"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}