Layer pseudospins,exhibiting quantum coherence and precise multistate controllability,present significant potential for the advancement of future computing technologies.In this work,we propose an in-memory probabilist...Layer pseudospins,exhibiting quantum coherence and precise multistate controllability,present significant potential for the advancement of future computing technologies.In this work,we propose an in-memory probabilistic computing scheme based on the electrical manipulation of layer pseudospins in layered materials,by exploiting the interaction between real spins and layer pseudospins.展开更多
As a typical in-memory computing hardware design, nonvolatile ternary content-addressable memories(TCAMs) enable the logic operation and data storage for high throughout in parallel big data processing. However,TCAM c...As a typical in-memory computing hardware design, nonvolatile ternary content-addressable memories(TCAMs) enable the logic operation and data storage for high throughout in parallel big data processing. However,TCAM cells based on conventional silicon-based devices suffer from structural complexity and large footprintlimitations. Here, we demonstrate an ultrafast nonvolatile TCAM cell based on the MoTe2/hBN/multilayergraphene (MLG) van der Waals heterostructure using a top-gated partial floating-gate field-effect transistor(PFGFET) architecture. Based on its ambipolar transport properties, the carrier type in the source/drain andcentral channel regions of the MoTe2 channel can be efficiently tuned by the control gate and top gate, respectively,enabling the reconfigurable operation of the device in either memory or FET mode. When working inthe memory mode, it achieves an ultrafast 60 ns programming/erase speed with a current on-off ratio of ∼105,excellent retention capability, and robust endurance. When serving as a reconfigurable transistor, unipolar p-typeand n-type FETs are obtained by adopting ultrafast 60 ns control-gate voltage pulses with different polarities.The monolithic integration of memory and logic within a single device enables the content-addressable memory(CAM) functionality. Finally, by integrating two PFGFETs in parallel, a TCAM cell with a high current ratioof ∼10^(5) between the match and mismatch states is achieved without requiring additional peripheral circuitry.These results provide a promising route for the design of high-performance TCAM devices for future in-memorycomputing applications.展开更多
Developing efficient neural network(NN)computing systems is crucial in the era of artificial intelligence(AI).Traditional von Neumann architectures have both the issues of"memory wall"and"power wall&quo...Developing efficient neural network(NN)computing systems is crucial in the era of artificial intelligence(AI).Traditional von Neumann architectures have both the issues of"memory wall"and"power wall",limiting the data transfer between memory and processing units[1,2].Compute-in-memory(CIM)technologies,particularly analogue CIM with memristor crossbars,are promising because of their high energy efficiency,computational parallelism,and integration density for NN computations[3].In practical applications,analogue CIM excels in tasks like speech recognition and image classification,revealing its unique advantages.For instance,it efficiently processes vast amounts of audio data in speech recognition,achieving high accuracy with minimal power consumption.In image classification,the high parallelism of analogue CIM significantly speeds up feature extraction and reduces processing time.With the boosting development of AI applications,the demands for computational accuracy and task complexity are rising continually.However,analogue CIM systems are limited in handling complex regression tasks with needs of precise floating-point(FP)calculations.They are primarily suited for the classification tasks with low data precision and a limited dynamic range[4].展开更多
Memristor-based memory devices are a solution to the energy efficiency bottleneck faced by von Neumann architectures through memory-computer fusion at the physical level[1].Compute-in-memory(CIM)technology achieves hi...Memristor-based memory devices are a solution to the energy efficiency bottleneck faced by von Neumann architectures through memory-computer fusion at the physical level[1].Compute-in-memory(CIM)technology achieves high-efficiency computation through the deep integration of memory and computing units via memristor crossbar arrays[2-4].Among them,analogue compute-in-memory(ACIM)technology capitalizes on the non-volatile and tunable resistive properties of memristive devices.展开更多
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
The emergence of generative artificial intelligence (AI) has catalyzed a new wave of intelligence development, resulting in significant growth in computing capabilities and intensifying competition for advanced comput...The emergence of generative artificial intelligence (AI) has catalyzed a new wave of intelligence development, resulting in significant growth in computing capabilities and intensifying competition for advanced computational power. In-Memory computing based on magnetic random-access memory (MRAM)offers advantages such as high speed and low power consumption is primed for enabling high-performance AI computing[1-6].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supported by the National Natural Science Foundation of China(Grant Nos.12322407,62122036,and 62034004)the Natural Science Foundation of Jiangsu Province(Grant No.BK20233001)+5 种基金the National Key R&D Program of China(Grant Nos.2023YFF0718400 and 2023YFF1203600)the Leading-edge Technology Program of Jiangsu Natural Science Foundation(Grant No.BK20232004)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB44000000)Innovation Program for Quantum Science and Technologysupport from the Fundamental Research Funds for the Central Universities(Grant Nos.020414380227,020414380240,and 020414380242)the e-Science Center of Collaborative Innovation Center of Advanced Microstructures。
文摘Layer pseudospins,exhibiting quantum coherence and precise multistate controllability,present significant potential for the advancement of future computing technologies.In this work,we propose an in-memory probabilistic computing scheme based on the electrical manipulation of layer pseudospins in layered materials,by exploiting the interaction between real spins and layer pseudospins.
基金supported by the National Key Research&Development Projects of China(Grant No.2022YFA1204100)National Natural Science Foundation of China(Grant No.62488201)+1 种基金CAS Project for Young Scientists in Basic Research(YSBR-003)the Innovation Program of Quantum Science and Technology(2021ZD0302700)。
文摘As a typical in-memory computing hardware design, nonvolatile ternary content-addressable memories(TCAMs) enable the logic operation and data storage for high throughout in parallel big data processing. However,TCAM cells based on conventional silicon-based devices suffer from structural complexity and large footprintlimitations. Here, we demonstrate an ultrafast nonvolatile TCAM cell based on the MoTe2/hBN/multilayergraphene (MLG) van der Waals heterostructure using a top-gated partial floating-gate field-effect transistor(PFGFET) architecture. Based on its ambipolar transport properties, the carrier type in the source/drain andcentral channel regions of the MoTe2 channel can be efficiently tuned by the control gate and top gate, respectively,enabling the reconfigurable operation of the device in either memory or FET mode. When working inthe memory mode, it achieves an ultrafast 60 ns programming/erase speed with a current on-off ratio of ∼105,excellent retention capability, and robust endurance. When serving as a reconfigurable transistor, unipolar p-typeand n-type FETs are obtained by adopting ultrafast 60 ns control-gate voltage pulses with different polarities.The monolithic integration of memory and logic within a single device enables the content-addressable memory(CAM) functionality. Finally, by integrating two PFGFETs in parallel, a TCAM cell with a high current ratioof ∼10^(5) between the match and mismatch states is achieved without requiring additional peripheral circuitry.These results provide a promising route for the design of high-performance TCAM devices for future in-memorycomputing applications.
文摘Developing efficient neural network(NN)computing systems is crucial in the era of artificial intelligence(AI).Traditional von Neumann architectures have both the issues of"memory wall"and"power wall",limiting the data transfer between memory and processing units[1,2].Compute-in-memory(CIM)technologies,particularly analogue CIM with memristor crossbars,are promising because of their high energy efficiency,computational parallelism,and integration density for NN computations[3].In practical applications,analogue CIM excels in tasks like speech recognition and image classification,revealing its unique advantages.For instance,it efficiently processes vast amounts of audio data in speech recognition,achieving high accuracy with minimal power consumption.In image classification,the high parallelism of analogue CIM significantly speeds up feature extraction and reduces processing time.With the boosting development of AI applications,the demands for computational accuracy and task complexity are rising continually.However,analogue CIM systems are limited in handling complex regression tasks with needs of precise floating-point(FP)calculations.They are primarily suited for the classification tasks with low data precision and a limited dynamic range[4].
文摘Memristor-based memory devices are a solution to the energy efficiency bottleneck faced by von Neumann architectures through memory-computer fusion at the physical level[1].Compute-in-memory(CIM)technology achieves high-efficiency computation through the deep integration of memory and computing units via memristor crossbar arrays[2-4].Among them,analogue compute-in-memory(ACIM)technology capitalizes on the non-volatile and tunable resistive properties of memristive devices.
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The emergence of generative artificial intelligence (AI) has catalyzed a new wave of intelligence development, resulting in significant growth in computing capabilities and intensifying competition for advanced computational power. In-Memory computing based on magnetic random-access memory (MRAM)offers advantages such as high speed and low power consumption is primed for enabling high-performance AI computing[1-6].
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.
