The dynamic neural network function realized by reconfigurable memristors to implement artificial neurons and synapses is an effective method to complete the next generation of neuromorphic computing.However,due to th...The dynamic neural network function realized by reconfigurable memristors to implement artificial neurons and synapses is an effective method to complete the next generation of neuromorphic computing.However,due to the limitation of reconfiguration conditions,there are inconsistencies in the turn-on voltage and operating current before and after the reconfiguration of neuromorphic devices,which leads to huge difficulties in hardware application development and is an urgent problem to be solved.In this work,we introduced light as a regulatory means in the memristor and achieved the reconfiguration of volatile(endurance~10^(6) cycles)and non-volatile(retention~10^(4 )s)characteristics with a unified working parameter through the photoelectric coupling mode.The switching voltage of the device can be controlled 100%by this method without any limiting current.This will allow neurons and synapses to be dynamically allocated on demand.We completed the verification such as Morse code decoding,Poisson coded image recognition,denoising in the image recognition process,and intelligent traffic signal recognition hardware system under different work modes.It is verified that the device can dynamically adjust the neuromorphic according to needs,providing a new idea for the further integration of neuromorphic computing in the future.展开更多
基金supported by Science and Technology Project of Hebei Education Department(grant no.QN2023092)High-level Talent Research Startup Project of Hebei University(grant no.521100221071,521000981426,521100223225)+19 种基金National Key R&D Plan"Nano Frontier"Key Special Project(Grant Nos.2024YFA1208400,2021YFA1200502)National Key R&D Program Disruptive Technology Innovation Project(Grant No.2024YFF1504300)National Natural Science Foundation of China(Grant Nos.62004056,62104058,Grant No.61874158)National Major R&D Project Cultivation Projects(Grant No.92164109)Natural Science Foundation of Hebei Province(Grant Nos.F2021201045,F2021201022,F2022201054,F2023201044,F2022201002)Special Support Funds for National High-Level Talents(Grant No.041500120001)Hebei Province Yanzhao Young Scientist Project(Grant No.F2023201076)Support Program for the Top Young Talents of Hebei Province(Grant No.70280011807)Hebei Province High-Level Talent Funding Project(Grant No.B20231003)Strategic Leading Science and Technology Special Project of Chinese Academy of Sciences(Grant No.XDB44000000-7)Interdisciplinary Research Program of Natural Science of Hebei University(Grant No.DXK202101)Institute of Life Sciences and Green Development(Grant No.521100311)Outstanding Young Scientific Research and Innovation Team of Hebei University(Grant No.605020521001)Advanced Talents Incubation Program of Hebei University(Grant Nos.521000981426,521100221071,521100224232,521000981363)Science and Technology Project of Hebei Education Department(Grant Nos.QN2020178,QN2021026)Baoding Science and Technology Plan Project(Grant No.2172P011)Hebei Province Key R&D Plan Projects(Grant No.22311101D)Baoding Science and Technology Plan Project(Grant No.2272P014)Regional Innovation and Development Joint Fund Key Project(Grant No.U23A20365)Hebei Province Natural Science Foundation(Grant No.F2023201044).
文摘The dynamic neural network function realized by reconfigurable memristors to implement artificial neurons and synapses is an effective method to complete the next generation of neuromorphic computing.However,due to the limitation of reconfiguration conditions,there are inconsistencies in the turn-on voltage and operating current before and after the reconfiguration of neuromorphic devices,which leads to huge difficulties in hardware application development and is an urgent problem to be solved.In this work,we introduced light as a regulatory means in the memristor and achieved the reconfiguration of volatile(endurance~10^(6) cycles)and non-volatile(retention~10^(4 )s)characteristics with a unified working parameter through the photoelectric coupling mode.The switching voltage of the device can be controlled 100%by this method without any limiting current.This will allow neurons and synapses to be dynamically allocated on demand.We completed the verification such as Morse code decoding,Poisson coded image recognition,denoising in the image recognition process,and intelligent traffic signal recognition hardware system under different work modes.It is verified that the device can dynamically adjust the neuromorphic according to needs,providing a new idea for the further integration of neuromorphic computing in the future.
