Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security application...Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security applications in the era of internet of things(IoT).In this review article,for achieving secure hardware systems in IoT,lowpower design techniques based on emerging memristive technology for hardware security primitives/systems are presented.By reviewing the state-of-the-art in three highlighted memristive application areas,i.e.memristive non-volatile memory,memristive reconfigurable logic computing and memristive artificial intelligent computing,their application-level impacts on the novel implementations of secret key generation,crypto functions and machine learning attacks are explored,respectively.For the low-power security applications in IoT,it is essential to understand how to best realize cryptographic circuitry using memristive circuitries,and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security.This review article aims to help researchers to explore security solutions,to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.展开更多
The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerg...The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.展开更多
基金supported by the DFG(German Research Foundation)Priority Program Nano Security,Project MemCrypto(Projektnummer 439827659/funding id DU 1896/2–1,PO 1220/15–1)the funding by the Fraunhofer Internal Programs under Grant No.Attract 600768。
文摘Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and inmemory computing(IMC),but there is a rising interest in using memristive technologies for security applications in the era of internet of things(IoT).In this review article,for achieving secure hardware systems in IoT,lowpower design techniques based on emerging memristive technology for hardware security primitives/systems are presented.By reviewing the state-of-the-art in three highlighted memristive application areas,i.e.memristive non-volatile memory,memristive reconfigurable logic computing and memristive artificial intelligent computing,their application-level impacts on the novel implementations of secret key generation,crypto functions and machine learning attacks are explored,respectively.For the low-power security applications in IoT,it is essential to understand how to best realize cryptographic circuitry using memristive circuitries,and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security.This review article aims to help researchers to explore security solutions,to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs.
基金This work was supported in part by the Ministry of Science and Technology of China under Grant No.2017YFA0206102in part by the National Natural Science Foundation of China under Grant No.61922083+2 种基金by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDB44000000by the European Union’s Horizon 2020 Research and Innovation Program with Grant Agreement No.824164by the German Research Foundation Projects MemCrypto under Grant No.GZ:DU 1896/2-1 and MemDPU under Grant No.GZ:DU 1896/3-1.
文摘The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.
