Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into accou...Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.展开更多
By definition, bionics is the application of biological mechanisms found in nature to artificial systems in order to achieve specific functional goals. Successful examples range from Velcro, the touch fastener inspire...By definition, bionics is the application of biological mechanisms found in nature to artificial systems in order to achieve specific functional goals. Successful examples range from Velcro, the touch fastener inspired by the hooks of burrs, to self-cleaning material, inspired by the surface of the lotus leaf. Recently, a new trend in bionics i Brain-Inspired Computing （BIC） - has captured increasing attention. Instead of learning from burrs and leaves, BIC aims to understand the brain and then utilize its operating principles to achieve powerful and efficient information processing.展开更多
Brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence(AGI),and a brain-inspired computing system is ...Brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence(AGI),and a brain-inspired computing system is a hierarchical system composed of neuromorphic chips,basic software and hardware,and algorithms/applications that embody this tech-nology.While the system is developing rapidly,it faces various challenges and opportunities brought by interdisciplinary research,including the issue of software and hardware fragmentation.This paper analyzes the status quo of brain-inspired computing systems.Enlightened by some design principle and methodology of general-purpose computers,it is proposed to construct"general-purpose"brain-inspired computing systems.A general-purpose brain-inspired computing system refers to a brain-inspired computing hierarchy constructed based on the design philosophy of decoupling software and hardware,which can flexibly support various brain-inspired computing applications and neuromorphic chips with different architec-tures.Further,this paper introduces our recent work in these aspects,including the ANN(artificial neural network)/SNN(spiking neural network)development tools,the hardware agnostic compilation infrastructure,and the chip micro-archi-tecture with high flexibility of programming and high performance;these studies show that the"general-purpose"system can remarkably improve the efficiency of application development and enhance the productivity of basic software,thereby being conductive to accelerating the advancement of various brain-inspired algorithms and applications.We believe that this is the key to the collaborative research and development,and the evolution of applications,basic software and chips in this field,and conducive to building a favorable software/hardware ecosystem of brain-inspired computing.展开更多
Brain-inspired computing refers to computational models,methods,and systems,that are mainly inspired by the processing mode or structure of brain.A recent study proposed the concept of"neuromorphic completeness&q...Brain-inspired computing refers to computational models,methods,and systems,that are mainly inspired by the processing mode or structure of brain.A recent study proposed the concept of"neuromorphic completeness"and the corresponding system hierarchy,which is helpful to determine the capability boundary of brain-inspired computing system and to judge whether hardware and software of brain-inspired computing are compatible with each other.As a position paper,this article analyzes the existing brain-inspired chips design characteristics and the current so-called"general purpose"application development frameworks for brain-inspired computing,as well as introduces the background and the potential of this proposal.Further,some key features of this concept are presented through the comparison with the Turing completeness and approximate computation,and the analyses of the relationship with"general-purpose"brain-inspired computing systems(it means that computing systems can support all computable applications).In the end,a promising technical approach to realize such computing systems is introduced,as well as the on-going research and the work foundation.We believe that this work is conducive to the design of extensible neuromorphic complete hardware-primitives and the corresponding chips.On this basis,it is expected to gradually realize"general purpose"brain-inspired computing system,in order to take into account the functionality completeness and application efficiency.展开更多
The demand of flexible neuromorphic computing electronics is increasing with the rapid development of wearable artificial intelligent devices.The flexible resistive random-access memory(RRAM)is one excellent candidate...The demand of flexible neuromorphic computing electronics is increasing with the rapid development of wearable artificial intelligent devices.The flexible resistive random-access memory(RRAM)is one excellent candidate of highdensity storage devices.However,due to the limitations of fabrication process,materials system and device structure,it is difficult to prepare flexible 3D highdensity network for neuromorphic computing.In this paper,a 3D flexible memristors network is developed via low-temperature atomic layer deposition(ALD)at 130C,with potential of extending to various flexible electronics.The typical bipolar switching characteristics are verified in RRAM units of 3D network,including first,second and third layers.Besides binary storage,the multibit storage in single unit is demonstrated and the storage density is further increased.As a connection link between binary storage and brain-inspired neuromorphic computing,the multibit storage capability paves the way for the tunable synaptic plasticity,for example,long-term potentiation/depression(LTP/LTD).The 3D memristors network successfully mimicked the typical neuromorphic functionality and realized ultra-multi conductance states modulation under 600 spikes.The robust mechanical flexibility is further demonstrated via LTP/LTD emulation under bending states(radius=10 mm).The 3D flexible memristors network shows significant potential of applications in high-performance,high-density and reliable wearable neuromorphic computing system.展开更多
In the post-Moore era,neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks.Memristors have been proposed as a key part of neuromorphic computing architectures,and can be used to emula...In the post-Moore era,neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks.Memristors have been proposed as a key part of neuromorphic computing architectures,and can be used to emulate the synaptic plasticities of the human brain.Ferroelectric memristors represent a breakthrough for memristive devices on account of their reliable nonvolatile storage,low write/read latency and tunable conductive states.However,among the reported ferroelectric memristors,the mechanisms of resistive switching are still under debate.In addition,there needs to be more research on emulation of the brain synapses using ferroelectric memristors.Herein,Cu/PbZr_(0.52)Ti_(0.48)O_(3)(PZT)/Pt ferroelectric memristors have been fabricated.The devices are able to realize the transformation from threshold switching behavior to resistive switching behavior.The synaptic plasticities,including excitatory post-synaptic current,paired-pulse facilitation,paired-pulse depression and spike time-dependent plasticity,have been mimicked by the PZT devices.Furthermore,the mechanisms of PZT devices have been investigated by first-principles calculations based on the interface barrier and conductive filament models.This work may contribute to the application of ferroelectric memristors in neuromorphic computing systems.展开更多
Reproducing the spatial cognition of animals using computational models that make agents navigate autonomously has attracted much attention. Many biologically inspired models for spatial cognition focus mainly on the ...Reproducing the spatial cognition of animals using computational models that make agents navigate autonomously has attracted much attention. Many biologically inspired models for spatial cognition focus mainly on the simulation of the hippocampus and only consider the effect of external environmental information(i.e., exogenous information) on the hippocampal coding. However, neurophysiological studies have shown that the striatum, which is closely related to the hippocampus, also plays an important role in spatial cognition and that information inside animals(i.e., endogenous information) also affects the encoding of the hippocampus. Inspired by the progress made in neurophysiological studies, we propose a new spatial cognitive model that consists of analogies between the hippocampus and striatum. This model takes into consideration how both exogenous and endogenous information affects coding by the environment. We carried out a series of navigation experiments that simulated a water maze and compared our model with other models. Our model is self-adaptable and robust and has better performance in navigation path length. We also discuss the possible reasons for the results and how our findings may help us understand real mechanisms in the spatial cognition of animals.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant No.61332003)High Performance Computing Laboratory,China(Grant No.201501-02)
文摘Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.
文摘By definition, bionics is the application of biological mechanisms found in nature to artificial systems in order to achieve specific functional goals. Successful examples range from Velcro, the touch fastener inspired by the hooks of burrs, to self-cleaning material, inspired by the surface of the lotus leaf. Recently, a new trend in bionics i Brain-Inspired Computing （BIC） - has captured increasing attention. Instead of learning from burrs and leaves, BIC aims to understand the brain and then utilize its operating principles to achieve powerful and efficient information processing.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.62250006,62072266,and 61836004the National Natural Science Foundation of China Youth Fund under Grant No.62202254,Beijing National Research Center for Information Science and Technology under Grant No.BNR2022RC01003+1 种基金the Tsinghua University Initiative Scientific Research Programthe Suzhou-Tsinghua Innovation Leadership Program.
文摘Brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence(AGI),and a brain-inspired computing system is a hierarchical system composed of neuromorphic chips,basic software and hardware,and algorithms/applications that embody this tech-nology.While the system is developing rapidly,it faces various challenges and opportunities brought by interdisciplinary research,including the issue of software and hardware fragmentation.This paper analyzes the status quo of brain-inspired computing systems.Enlightened by some design principle and methodology of general-purpose computers,it is proposed to construct"general-purpose"brain-inspired computing systems.A general-purpose brain-inspired computing system refers to a brain-inspired computing hierarchy constructed based on the design philosophy of decoupling software and hardware,which can flexibly support various brain-inspired computing applications and neuromorphic chips with different architec-tures.Further,this paper introduces our recent work in these aspects,including the ANN(artificial neural network)/SNN(spiking neural network)development tools,the hardware agnostic compilation infrastructure,and the chip micro-archi-tecture with high flexibility of programming and high performance;these studies show that the"general-purpose"system can remarkably improve the efficiency of application development and enhance the productivity of basic software,thereby being conductive to accelerating the advancement of various brain-inspired algorithms and applications.We believe that this is the key to the collaborative research and development,and the evolution of applications,basic software and chips in this field,and conducive to building a favorable software/hardware ecosystem of brain-inspired computing.
基金partly supported by the National Natural Science Foundation of China(Nos.62072266 and 62050340)Beijing Academy of Artificial Intelligence(No.BAAI2019ZD0403)。
文摘Brain-inspired computing refers to computational models,methods,and systems,that are mainly inspired by the processing mode or structure of brain.A recent study proposed the concept of"neuromorphic completeness"and the corresponding system hierarchy,which is helpful to determine the capability boundary of brain-inspired computing system and to judge whether hardware and software of brain-inspired computing are compatible with each other.As a position paper,this article analyzes the existing brain-inspired chips design characteristics and the current so-called"general purpose"application development frameworks for brain-inspired computing,as well as introduces the background and the potential of this proposal.Further,some key features of this concept are presented through the comparison with the Turing completeness and approximate computation,and the analyses of the relationship with"general-purpose"brain-inspired computing systems(it means that computing systems can support all computable applications).In the end,a promising technical approach to realize such computing systems is introduced,as well as the on-going research and the work foundation.We believe that this work is conducive to the design of extensible neuromorphic complete hardware-primitives and the corresponding chips.On this basis,it is expected to gradually realize"general purpose"brain-inspired computing system,in order to take into account the functionality completeness and application efficiency.
基金This work was supported by the NSFC(61704030 and 61522404)Shanghai Rising-Star Program(19QA1400600)+1 种基金the Program of Shanghai Subject Chief Scientist(18XD1402800)the Support Plans for the Youth Top-Notch Talents of China.
文摘The demand of flexible neuromorphic computing electronics is increasing with the rapid development of wearable artificial intelligent devices.The flexible resistive random-access memory(RRAM)is one excellent candidate of highdensity storage devices.However,due to the limitations of fabrication process,materials system and device structure,it is difficult to prepare flexible 3D highdensity network for neuromorphic computing.In this paper,a 3D flexible memristors network is developed via low-temperature atomic layer deposition(ALD)at 130C,with potential of extending to various flexible electronics.The typical bipolar switching characteristics are verified in RRAM units of 3D network,including first,second and third layers.Besides binary storage,the multibit storage in single unit is demonstrated and the storage density is further increased.As a connection link between binary storage and brain-inspired neuromorphic computing,the multibit storage capability paves the way for the tunable synaptic plasticity,for example,long-term potentiation/depression(LTP/LTD).The 3D memristors network successfully mimicked the typical neuromorphic functionality and realized ultra-multi conductance states modulation under 600 spikes.The robust mechanical flexibility is further demonstrated via LTP/LTD emulation under bending states(radius=10 mm).The 3D flexible memristors network shows significant potential of applications in high-performance,high-density and reliable wearable neuromorphic computing system.
基金Jiangsu Province Research Foundation(Grant Nos.BK20191202,RK106STP18003,and SZDG2018007)the Jiangsu Province Research Foundation(Grant Nos.BK20191202,RK106STP18003,and SZDG2018007)+1 种基金the Research Innovation Program for College Graduates of Jiangsu Province(Grant Nos.KYCX200806,KYCX190960,and SJCX190268)NJUPTSF(Grant Nos.NY217116,NY220078,and NY218107)。
文摘In the post-Moore era,neuromorphic computing has been mainly focused on breaking the von Neumann bottlenecks.Memristors have been proposed as a key part of neuromorphic computing architectures,and can be used to emulate the synaptic plasticities of the human brain.Ferroelectric memristors represent a breakthrough for memristive devices on account of their reliable nonvolatile storage,low write/read latency and tunable conductive states.However,among the reported ferroelectric memristors,the mechanisms of resistive switching are still under debate.In addition,there needs to be more research on emulation of the brain synapses using ferroelectric memristors.Herein,Cu/PbZr_(0.52)Ti_(0.48)O_(3)(PZT)/Pt ferroelectric memristors have been fabricated.The devices are able to realize the transformation from threshold switching behavior to resistive switching behavior.The synaptic plasticities,including excitatory post-synaptic current,paired-pulse facilitation,paired-pulse depression and spike time-dependent plasticity,have been mimicked by the PZT devices.Furthermore,the mechanisms of PZT devices have been investigated by first-principles calculations based on the interface barrier and conductive filament models.This work may contribute to the application of ferroelectric memristors in neuromorphic computing systems.
基金by National Natural Science Foundation of China(Nos.61773027 and 62076014)National Key Research and Development Program Project(No.2020YFB1005903)Industrial Internet Innovation and Development Project(No.135060009002).
文摘Reproducing the spatial cognition of animals using computational models that make agents navigate autonomously has attracted much attention. Many biologically inspired models for spatial cognition focus mainly on the simulation of the hippocampus and only consider the effect of external environmental information(i.e., exogenous information) on the hippocampal coding. However, neurophysiological studies have shown that the striatum, which is closely related to the hippocampus, also plays an important role in spatial cognition and that information inside animals(i.e., endogenous information) also affects the encoding of the hippocampus. Inspired by the progress made in neurophysiological studies, we propose a new spatial cognitive model that consists of analogies between the hippocampus and striatum. This model takes into consideration how both exogenous and endogenous information affects coding by the environment. We carried out a series of navigation experiments that simulated a water maze and compared our model with other models. Our model is self-adaptable and robust and has better performance in navigation path length. We also discuss the possible reasons for the results and how our findings may help us understand real mechanisms in the spatial cognition of animals.
