It is still challenging to fully integrate computing in memory chip as edge learning devices.In recent work published on Science,a fully-integrated chip based on neuromorphic memristors was developed for edge learning...It is still challenging to fully integrate computing in memory chip as edge learning devices.In recent work published on Science,a fully-integrated chip based on neuromorphic memristors was developed for edge learning as artificial neural networks with functionality of synapses,dendrites,and somas.A crossbar-array memristor chip facilitated edge learning including hardware realization,learning algorithm,and cycle-parallel sign-and threshold-based learning(STELLAR)scheme.The motion control and demonstration platforms were executed to improve the edge learning ability for adapting to new scenarios.展开更多
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
In this paper, we review the recent trends in parallel search and artificial intelligence (AI) applications using emerging non-volatile ternary content addressable memory (TCAM). Firstly, the principle and development...In this paper, we review the recent trends in parallel search and artificial intelligence (AI) applications using emerging non-volatile ternary content addressable memory (TCAM). Firstly, the principle and development of four typical emerging memory used to implement the non-volatile TCAM are discussed. Then, we analyze the principle and challenges of SRAM-based TCAM and non-volatile TCAM for the parallel search. Finally, the research trends and challenges of non-volatile TCAM used for AI application are presented, which include computer-science oriented and neuroscience oriented computing.展开更多
基金funding support from the National Natural Science Foundation of China(52172205).
文摘It is still challenging to fully integrate computing in memory chip as edge learning devices.In recent work published on Science,a fully-integrated chip based on neuromorphic memristors was developed for edge learning as artificial neural networks with functionality of synapses,dendrites,and somas.A crossbar-array memristor chip facilitated edge learning including hardware realization,learning algorithm,and cycle-parallel sign-and threshold-based learning(STELLAR)scheme.The motion control and demonstration platforms were executed to improve the edge learning ability for adapting to new scenarios.
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.
文摘In this paper, we review the recent trends in parallel search and artificial intelligence (AI) applications using emerging non-volatile ternary content addressable memory (TCAM). Firstly, the principle and development of four typical emerging memory used to implement the non-volatile TCAM are discussed. Then, we analyze the principle and challenges of SRAM-based TCAM and non-volatile TCAM for the parallel search. Finally, the research trends and challenges of non-volatile TCAM used for AI application are presented, which include computer-science oriented and neuroscience oriented computing.
