E. A. Ryndin, I. A. Mavrin, N. V. Andreeva, V. V. Luchinin
{"title":"基于 Memristor 电子元件基础的神经形态电子模块用于图像识别","authors":"E. A. Ryndin, I. A. Mavrin, N. V. Andreeva, V. V. Luchinin","doi":"10.1134/S2635167623600724","DOIUrl":null,"url":null,"abstract":"<div><p>In this work a method for increasing the efficiency of hardware implementation and minimizing the number of electronic synaptic elements of asynchronous spiking neural networks in solving image identification problems is developed. The effectiveness of the proposed method is demonstrated in the process of optimizing the parameters of neurons and training the neural network on the developed software model and confirmed by the results of SPICE modeling and measurement of the signals of the neural network implemented on series electronic components.</p></div>","PeriodicalId":716,"journal":{"name":"Nanotechnologies in Russia","volume":"18 1 supplement","pages":"S194 - S202"},"PeriodicalIF":0.8000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Neuromorphic Electronic Module Based on the Use of the Memristor Electronic-Component Base for Image Recognition\",\"authors\":\"E. A. Ryndin, I. A. Mavrin, N. V. Andreeva, V. V. Luchinin\",\"doi\":\"10.1134/S2635167623600724\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this work a method for increasing the efficiency of hardware implementation and minimizing the number of electronic synaptic elements of asynchronous spiking neural networks in solving image identification problems is developed. The effectiveness of the proposed method is demonstrated in the process of optimizing the parameters of neurons and training the neural network on the developed software model and confirmed by the results of SPICE modeling and measurement of the signals of the neural network implemented on series electronic components.</p></div>\",\"PeriodicalId\":716,\"journal\":{\"name\":\"Nanotechnologies in Russia\",\"volume\":\"18 1 supplement\",\"pages\":\"S194 - S202\"},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2024-03-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nanotechnologies in Russia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S2635167623600724\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nanotechnologies in Russia","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1134/S2635167623600724","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
Neuromorphic Electronic Module Based on the Use of the Memristor Electronic-Component Base for Image Recognition
In this work a method for increasing the efficiency of hardware implementation and minimizing the number of electronic synaptic elements of asynchronous spiking neural networks in solving image identification problems is developed. The effectiveness of the proposed method is demonstrated in the process of optimizing the parameters of neurons and training the neural network on the developed software model and confirmed by the results of SPICE modeling and measurement of the signals of the neural network implemented on series electronic components.
期刊介绍:
Nanobiotechnology Reports publishes interdisciplinary research articles on fundamental aspects of the structure and properties of nanoscale objects and nanomaterials, polymeric and bioorganic molecules, and supramolecular and biohybrid complexes, as well as articles that discuss technologies for their preparation and processing, and practical implementation of products, devices, and nature-like systems based on them. The journal publishes original articles and reviews that meet the highest scientific quality standards in the following areas of science and technology studies: self-organizing structures and nanoassemblies; nanostructures, including nanotubes; functional and structural nanomaterials; polymeric, bioorganic, and hybrid nanomaterials; devices and products based on nanomaterials and nanotechnology; nanobiology and genetics, and omics technologies; nanobiomedicine and nanopharmaceutics; nanoelectronics and neuromorphic computing systems; neurocognitive systems and technologies; nanophotonics; natural science methods in a study of cultural heritage items; metrology, standardization, and monitoring in nanotechnology.