{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T17:13:25Z","timestamp":1778087605345,"version":"3.51.4"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032253101","type":"print"},{"value":"9783032253118","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-25311-8_10","type":"book-chapter","created":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:33:49Z","timestamp":1778085229000},"page":"128-143","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Heterogeneous SoC Integrating an\u00a0Open-Source Recurrent SNN Accelerator for\u00a0Neuromorphic Edge Computing on\u00a0FPGA"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7894-5006","authenticated-orcid":false,"given":"Michelangelo","family":"Barocci","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9175-2838","authenticated-orcid":false,"given":"Vittorio","family":"Fra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9046-5618","authenticated-orcid":false,"given":"Enrico","family":"Macii","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2672-7593","authenticated-orcid":false,"given":"Gianvito","family":"Urgese","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Barocci, M., Fra, V., Macii, E., Urgese, G.: Review of open neuromorphic architectures and a first integration in the RISC-V PULP platform. In: 2023 IEEE 16th International Symposium on Embedded Multicore\/Many-core Systems-on-Chip (MCSoC) (2023)","DOI":"10.1109\/MCSoC60832.2023.00076"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Bartolozzi, C., Indiveri, G., Donati, E.: Embodied neuromorphic intelligence. Nat. Commun. 13 (2022)","DOI":"10.1038\/s41467-022-28487-2"},{"key":"10_CR3","unstructured":"Bellec, G., Salaj, D., Subramoney, A., Legenstein, R., Maass, W.: Long short-term memory and learning-to-learn in networks of spiking neurons. In: Advances in Neural Information Processing Systems (2018)"},{"issue":"1","key":"10_CR4","doi-asserted-by":"publisher","first-page":"3625","DOI":"10.1038\/s41467-020-17236-y","volume":"11","author":"G Bellec","year":"2020","unstructured":"Bellec, G., et al.: A solution to the learning dilemma for recurrent networks of spiking neurons. Nat. Commun. 11(1), 3625 (2020). https:\/\/doi.org\/10.1038\/s41467-020-17236-y","journal-title":"Nat. Commun."},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Bos, H., Muir, D.: Sub-mw neuromorphic SNN audio processing applications with rockpool and xylo. In: Embedded Artificial Intelligence. River Publishers (2023)","DOI":"10.1201\/9781003394440-7"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Carpegna, A., Savino, A., Carlo, S.D.: Spiker+: a framework for the generation of efficient Spiking Neural Networks FPGA accelerators for inference at the edge. arXiv preprint arXiv:2401.01141 (2024)","DOI":"10.1109\/TETC.2024.3511676"},{"key":"10_CR7","doi-asserted-by":"publisher","unstructured":"Cheung, K., Schultz, S.R., Luk, W.: Neuroflow: A general purpose spiking neural network simulation platform using customizable processors. Front. Neurosci. 9 (2016). https:\/\/doi.org\/10.3389\/fnins.2015.00516","DOI":"10.3389\/fnins.2015.00516"},{"key":"10_CR8","doi-asserted-by":"publisher","unstructured":"Clair, J., Eichler, G., Carloni, L.P.: Spikehard: Efficiency-driven neuromorphic hardware for heterogeneous systems-on-chip. ACM Trans. Embed. Comput. Syst. 22(5s) (2023). https:\/\/doi.org\/10.1145\/3609101","DOI":"10.1145\/3609101"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Davies, M., et al.: Loihi: a Neuromorphic Manycore Processor with On-Chip Learning. IEEE Micro 38 (2018)","DOI":"10.1109\/MM.2018.112130359"},{"key":"10_CR10","doi-asserted-by":"publisher","unstructured":"Frenkel, C., Indiveri, G.: Reckon: a 28nm sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales. In: 2022 IEEE International Solid-State Circuits Conference (ISSCC). vol.\u00a065, pp.\u00a01\u20133 (2022). https:\/\/doi.org\/10.1109\/ISSCC42614.2022.9731734","DOI":"10.1109\/ISSCC42614.2022.9731734"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Indiveri, G., et\u00a0al.: Neuromorphic silicon neuron circuits. Front. Neurosci. 5 (2011)","DOI":"10.3389\/fnins.2011.00118"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Linares-Barranco, A., Prono, L., Lengenstein, R., Indiveri, G., Frenkel, C.: Adaptive robotic arm control with a spiking recurrent neural network on a digital accelerator. arXiv preprint arXiv:2405.12849 (2024)","DOI":"10.1109\/ICECS61496.2024.10849226"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Maass, W.: Networks of spiking neurons: The third generation of neural network models. Neural Netw. 10 (1997)","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Machetti, S., et al.: X-heep: An open-source, configurable and extendible risc-v microcontroller for the exploration of ultra-low-power edge accelerators (2024)","DOI":"10.1109\/ISVLSI65124.2025.11130281"},{"key":"10_CR15","doi-asserted-by":"publisher","unstructured":"Mack, J., et al.: Ranc: Reconfigurable architecture for neuromorphic computing. Trans. Comp.-Aided Des. Integ. Cir. Sys. 40(11), 2265\u20132278 (2021). https:\/\/doi.org\/10.1109\/TCAD.2020.3038151","DOI":"10.1109\/TCAD.2020.3038151"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Markovi\u0107, D., Mizrahi, A., Querlioz, D., Grollier, J.: Physics for neuromorphic computing. Nat. Rev. Physics 2 (2020)","DOI":"10.1038\/s42254-020-0208-2"},{"key":"10_CR17","unstructured":"Mayr, C., Hoeppner, S., Furber, S.: Spinnaker 2: A 10 million core processor system for brain simulation and machine learning. arXiv preprint arXiv:1911.02385 (2019)"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"M\u00fcller-Cleve, S.F., et al.: Braille letter reading: A benchmark for spatio-temporal pattern recognition on neuromorphic hardware. Front. Neurosc. 16 (2022)","DOI":"10.3389\/fnins.2022.951164"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Orchard, G., et al.: Efficient Neuromorphic Signal Processing with Loihi 2. In: IEEE Workshop on Signal Processing Systems (SiPS). vol. 2021 (2021)","DOI":"10.1109\/SiPS52927.2021.00053"},{"key":"10_CR20","doi-asserted-by":"publisher","unstructured":"Panchapakesan, S., Fang, Z., Li, J.: Syncnn: Evaluating and accelerating spiking neural networks on fpgas. ACM Trans. Reconfigurable Technol. Syst. 15(4) (2022). https:\/\/doi.org\/10.1145\/3514253","DOI":"10.1145\/3514253"},{"issue":"1","key":"10_CR21","doi-asserted-by":"publisher","first-page":"8122","DOI":"10.1038\/s41467-024-52259-9","volume":"15","author":"JE Pedersen","year":"2024","unstructured":"Pedersen, J.E., Abreu, S., Jobst, M., Lenz, G., Fra, V., Bauer, F.C., Muir, D.R., Zhou, P., Vogginger, B., Heckel, K., et al.: Neuromorphic intermediate representation: a unified instruction set for interoperable brain-inspired computing. Nat. Commun. 15(1), 8122 (2024)","journal-title":"Nat. Commun."},{"key":"10_CR22","doi-asserted-by":"publisher","unstructured":"Pham, Q.T., Nguyen, T.Q., Hoang, P.C., Dang, Q.H., Nguyen, D.M., Nguyen, H.H.: A review of SNN implementation on FPGA. In: 2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pp.\u00a01\u20136 (2021). https:\/\/doi.org\/10.1109\/MAPR53640.2021.9585245","DOI":"10.1109\/MAPR53640.2021.9585245"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Schmidgall, S., Ziaei, R., Achterberg, J., Kirsch, L., Hajiseyedrazi, S., Eshraghian, J.: Brain-inspired learning in artificial neural networks: a review. APL Mach. Learn. 2 (2024)","DOI":"10.1063\/5.0186054"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Schuman, C.D., Kulkarni, S.R., Parsa, M., Mitchell, J.P., Kay, B., et\u00a0al.: Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2 (2022)","DOI":"10.1038\/s43588-021-00184-y"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Siddique, A., Vai, M.I., Pun, S.H.: A low cost neuromorphic learning engine based on a high performance supervised SNN learning algorithm. Sci. Rep. 13 (2023)","DOI":"10.1038\/s41598-023-32120-7"},{"key":"10_CR26","doi-asserted-by":"publisher","unstructured":"Trensch, G., Morrison, A.: A system-on-chip based hybrid neuromorphic compute node architecture for reproducible hyper-real-time simulations of spiking neural networks. Front. Neuroinf. 16 (2022). https:\/\/doi.org\/10.3389\/fninf.2022.884033","DOI":"10.3389\/fninf.2022.884033"},{"key":"10_CR27","doi-asserted-by":"publisher","first-page":"1197918","DOI":"10.3389\/fnins.2023.1197918","volume":"17","author":"G Urgese","year":"2023","unstructured":"Urgese, G., Rios-Navarro, A., Linares-Barranco, A., Stewart, T.C., Michmizos, K.: Powering the next-generation IoT applications: new tools and emerging technologies for the development of neuromorphic system of systems. Front. Neurosci. 17, 1197918 (2023)","journal-title":"Front. Neurosci."},{"key":"10_CR28","doi-asserted-by":"publisher","unstructured":"Yousefzadeh, A., et al.: Seneca: Scalable energy-efficient neuromorphic computer architecture. In: 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS), pp. 371\u2013374 (2022). https:\/\/doi.org\/10.1109\/AICAS54282.2022.9870025","DOI":"10.1109\/AICAS54282.2022.9870025"},{"key":"10_CR29","doi-asserted-by":"publisher","unstructured":"Zaruba, F., Benini, L.: The cost of application-class processing: Energy and performance analysis of a linux-ready 1.7-GHZ 64-bit risc-v core in 22-nm fdsoi technology. IEEE Trans. Very Large Scale Integration (VLSI) Syst. 27(11), 2629\u20132640 (2019). https:\/\/doi.org\/10.1109\/TVLSI.2019.2926114","DOI":"10.1109\/TVLSI.2019.2926114"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-25311-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T16:34:00Z","timestamp":1778085240000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-25311-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032253101","9783032253118"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-25311-8_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}