{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:30:29Z","timestamp":1773354629030,"version":"3.50.1"},"reference-count":19,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF) grant for RLRC funded by the Korea government (MSIT)","award":["2022R1A5A8026986"],"award-info":[{"award-number":["2022R1A5A8026986"]}]},{"name":"National Research Foundation of Korea (NRF) grant for RLRC funded by the Korea government (MSIT)","award":["2020-0-01304"],"award-info":[{"award-number":["2020-0-01304"]}]},{"name":"National Research Foundation of Korea (NRF) grant for RLRC funded by the Korea government (MSIT)","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}]},{"name":"National Research Foundation of Korea (NRF) grant for RLRC funded by the Korea government (MSIT)","award":["2020M3H2A1076786"],"award-info":[{"award-number":["2020M3H2A1076786"]}]},{"name":"MSIT","award":["2022R1A5A8026986"],"award-info":[{"award-number":["2022R1A5A8026986"]}]},{"name":"MSIT","award":["2020-0-01304"],"award-info":[{"award-number":["2020-0-01304"]}]},{"name":"MSIT","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}]},{"name":"MSIT","award":["2020M3H2A1076786"],"award-info":[{"award-number":["2020M3H2A1076786"]}]},{"name":"MSIT (Ministry of Science and ICT), Korea","award":["2022R1A5A8026986"],"award-info":[{"award-number":["2022R1A5A8026986"]}]},{"name":"MSIT (Ministry of Science and ICT), Korea","award":["2020-0-01304"],"award-info":[{"award-number":["2020-0-01304"]}]},{"name":"MSIT (Ministry of Science and ICT), Korea","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}]},{"name":"MSIT (Ministry of Science and ICT), Korea","award":["2020M3H2A1076786"],"award-info":[{"award-number":["2020M3H2A1076786"]}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","award":["2022R1A5A8026986"],"award-info":[{"award-number":["2022R1A5A8026986"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","award":["2020-0-01304"],"award-info":[{"award-number":["2020-0-01304"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","award":["IITP-2023-2020-0-01462"],"award-info":[{"award-number":["IITP-2023-2020-0-01462"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","award":["2020M3H2A1076786"],"award-info":[{"award-number":["2020M3H2A1076786"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents a compact analog system-on-chip (SoC) implementation of a spiking neural network (SNN) for low-power Internet of Things (IoT) applications. The low-power implementation of an SNN SoC requires the optimization of not only the SNN model but also the architecture and circuit designs. In this work, the SNN has been constituted from the analog neuron and synaptic circuits, which are designed to optimize both the chip area and power consumption. The proposed synapse circuit is based on a current multiplier charge injector (CMCI) circuit, which can significantly reduce power consumption and chip area compared with the previous work while allowing for design scalability for higher resolutions. The proposed neuron circuit employs an asynchronous structure, which makes it highly sensitive to input synaptic currents and enables it to achieve higher energy efficiency. To compare the performance of the proposed SoC in its area and power consumption, we implemented a digital SoC for the same SNN model in FPGA. The proposed SNN chip, when trained using the MNIST dataset, achieves a classification accuracy of 96.56%. The presented SNN chip has been implemented using a 65 nm CMOS process for fabrication. The entire chip occupies 0.96 mm2 and consumes an average power of 530 \u03bcW, which is 200 times lower than its digital counterpart.<\/jats:p>","DOI":"10.3390\/s23146275","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T01:58:14Z","timestamp":1689040694000},"page":"6275","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Compact and Low-Power SoC Design for Spiking Neural Network Based on Current Multiplier Charge Injector Synapse"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1313-2419","authenticated-orcid":false,"given":"Malik Summair","family":"Asghar","sequence":"first","affiliation":[{"name":"Department of Electronics, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea"},{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad Campus, Univeristy Road, Tobe Camp., Abbottabad 22044, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4038-462X","authenticated-orcid":false,"given":"Saad","family":"Arslan","sequence":"additional","affiliation":[{"name":"TSY Design (Pvt.) Ltd., Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali A.","family":"Al-Hamid","sequence":"additional","affiliation":[{"name":"Department of Electronics, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HyungWon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronics, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.giq.2019.02.003","article-title":"IoT and AI for smart government: A research agenda","volume":"36","author":"Kankanhalli","year":"2019","journal-title":"Gov. Inf. Q."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1109\/5.58356","article-title":"Neuromorphic electronic systems","volume":"78","author":"Mead","year":"1990","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1038\/nature14441","article-title":"Training and operation of an integrated neuromorphic network based on metal-oxide memristors","volume":"521","author":"Prezioso","year":"2015","journal-title":"Nature"},{"key":"ref_4","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet classification with deep convolutional neural networks. Proceedings of the Advances in NIPS 25, Lake Tahoe, NV, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"129","DOI":"10.5573\/JSTS.2019.19.1.129","article-title":"A low-power, mixed-mode neural network classifier for robust scene classification","volume":"19","author":"Lee","year":"2019","journal-title":"J. Semicond. Technol. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/85.238389","article-title":"First draft of a report on the EDVAC","volume":"15","year":"1993","journal-title":"IEEE Ann. Hist. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MSPEC.2017.7934228","article-title":"Special report: Can we copy the brain?\u2014The brain as computer","volume":"54","author":"Meier","year":"2017","journal-title":"IEEE Spectr."},{"key":"ref_8","unstructured":"Mead, C. (1989). Analog VLSI and Neural Systems, Addison-Wesley. [1st ed.]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"40420","DOI":"10.1021\/acsami.7b11191","article-title":"Analog synaptic behavior of a silicon nitride memristor","volume":"9","author":"Kim","year":"2017","journal-title":"ACS Appl. Mater. Interfaces"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Miyashita, D., Kousai, S., Suzuki, T., and Deguchi, J. (2016, January 7\u20139). Time-Domain Neural Network: A 48.5 TSOp\/s\/W neuromorphic chip optimized for deep learning and CMOS technology. Proceedings of the IEEE Asian SSC Conference, Toyama, Japan.","DOI":"10.1109\/ASSCC.2016.7844126"},{"key":"ref_11","first-page":"4299","article-title":"An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture","volume":"65","author":"Aamir","year":"2018","journal-title":"IEEE TCAS I Regul. Pap."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1038\/s41467-018-04484-2","article-title":"Efficient and self-adaptive in-situ learning in multilayer memristor neural networks","volume":"9","author":"Li","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Asghar, M.S., Arslan, S., and Kim, H. (2021, January 22\u201328). Current multiplier based synapse and neuron circuits for compact SNN chip. Proceedings of the IEEE ISCAS, Daegu, Republic of Korea.","DOI":"10.1109\/ISCAS51556.2021.9401173"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Asghar, M.S., Arslan, S., and Kim, H. (2021). A Low-Power Spiking Neural Network Chip Based on a Compact LIF Neuron and Binary Exponential Charge Injector Synapse Circuits. Sensors, 21.","DOI":"10.3390\/s21134462"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Camu\u00f1as-Mesa, L.A., Linares-Barranco, B., and Serrano-Gotarredona, T. (2019). Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations. Materials, 12.","DOI":"10.3390\/ma12172745"},{"key":"ref_16","unstructured":"Jolivet, R., Rauch, A., L\u00fcscher, H.-R., and Gerstner, W. (2005, January 5\u20138). Integrate-and-fire models with adaptation are good enough: Predicting spike times under random current injection. Proceedings of the NIPS 18, Vancouver, BC, Canada. Available online: https:\/\/proceedings.neurips.cc\/paper\/2005."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","article-title":"Simple model of spiking neurons","volume":"14","author":"Izhikevich","year":"2003","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Al-Hamid, A.A., and Kim, H. (2020). Optimization of Spiking Neural Networks Based on Binary Streamed Rate Coding. Electronics, 9.","DOI":"10.3390\/electronics9101599"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/TNN.2005.860850","article-title":"A VLSI Array of Low-Power Spiking Neurons and Bistable Synapses with Spike-Timing Dependent Plasticity","volume":"17","author":"Indiveri","year":"2006","journal-title":"IEEE Trans. Neural Netw."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6275\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:09:56Z","timestamp":1760126996000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/14\/6275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":19,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23146275"],"URL":"https:\/\/doi.org\/10.3390\/s23146275","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,10]]}}}