In the 9 December 2024 issue of Nature[1],a team of Google engineers reported breakthrough results using“Willow”,their lat-est quantum computing chip(Fig.1).By meeting a milestone“below threshold”reduction in the ...In the 9 December 2024 issue of Nature[1],a team of Google engineers reported breakthrough results using“Willow”,their lat-est quantum computing chip(Fig.1).By meeting a milestone“below threshold”reduction in the rate of errors that plague super-conducting circuit-based quantum computing systems(Fig.2),the work moves the field another step towards its promised super-charged applications,albeit likely still many years away.Areas expected to benefit from quantum computing include,among others,drug discovery,materials science,finance,cybersecurity,and machine learning.展开更多
Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural n...Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems.However,developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge.Organic computational materials offer affordable,biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching.Here,the review investigates the advancements made in the development of organic neuromorphic devices.This review explores resistive switching mechanisms such as interface-regulated filament growth,molecular-electronic dynamics,nanowire-confined filament growth,and vacancy-assisted ion migration,while proposing methodologies to enhance state retention and conductance adjustment.The survey examines the challenges faced in implementing low-power neuromorphic computing,e.g.,reducing device size and improving switching time.The review analyses the potential of these materials in adjustable,flexible,and low-power consumption applications,viz.biohybrid spiking circuits interacting with biological systems,systems that respond to specific events,robotics,intelligent agents,neuromorphic computing,neuromorphic bioelectronics,neuroscience,and other applications,and prospects of this technology.展开更多
Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap fr...Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models.This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance.Therefore,algorithm-hardware co-design has emerged as the primary methodology,which ana-lyzes algorithm behaviors on hardware to identify common computational properties.These properties can motivate algo-rithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency.We then reviewed recent works on robotic and embodied AI algorithms and computing hard-ware to demonstrate this algorithm-hardware co-design methodology.In the end,we discuss future research opportunities by answering two questions:(1)how to adapt the computing platforms to the rapid evolution of embodied AI algorithms,and(2)how to transform the potential of emerging hardware innovations into end-to-end inference improvements.展开更多
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
This paper describes a 2D/3D vision chip with integrated sensing and processing capabilities.The 2D/3D vision chip architecture includes a 2D/3D image sensor and a programmable visual processor.In this architecture,we...This paper describes a 2D/3D vision chip with integrated sensing and processing capabilities.The 2D/3D vision chip architecture includes a 2D/3D image sensor and a programmable visual processor.In this architecture,we design a novel on-chip processing flow with die-to-die image transmission and low-latency fixed-point image processing.The vision chip achieves real-time end-to-end processing of convolutional neural networks(CNNs)and conventional image processing algo-rithms.Furthermore,an end-to-end 2D/3D vision system is built to exhibit the capacity of the vision chip.The vision system achieves real-timing applications under 2D and 3D scenes,such as human face detection(processing delay 10.2 ms)and depth map reconstruction(processing delay 4.1 ms).The frame rate of image acquisition,image process,and result display is larger than 30 fps.展开更多
This paper introduces a new datapath architecture for reconfigurable processors. The proposed datapath is based on Network-on-Chip approach and facilitates tight coupling of all functional units. Reconfigurable functi...This paper introduces a new datapath architecture for reconfigurable processors. The proposed datapath is based on Network-on-Chip approach and facilitates tight coupling of all functional units. Reconfigurable functional elements can be dynamically allocated for application specific optimizations, enabling polymorphic computing. Using a modified network simulator, performance of several NoC topologies and parameters are investigated with standard benchmark programs, including fine grain and coarse grain computations. Simulation results highlight the flexibility and scalability of the proposed polymorphic NoC processor for a wide range of application domains.展开更多
A method for determination of tool-chip contact length is theoreticallypresented in orthogonal metal machining. By using computer simulation and based on the analyses ofthe elastro-plastic deformation with lagrangian ...A method for determination of tool-chip contact length is theoreticallypresented in orthogonal metal machining. By using computer simulation and based on the analyses ofthe elastro-plastic deformation with lagrangian finite element method in the deformation zone, theaccumulated representative length of the low layer, the tool-chip contact length of the chipcontacting the tool rake are calculated, experimental studies are also carried out with 0.2 percentcarbon steel. It is shown that the tool-chip contact lengths obtained from computer simulation havea good agreement with those of measured values.展开更多
Developed a new program structure using in single chip computer system, which based on multitasking mechanism. Discussed the specific method for realization of the new structure. The applied sample is also provided.
In order to cool computer chip efficiently with the least noise, a single phase water-cooling radiator for computer chip driven by piezoelectric pump with two parallel-connection chambers is developed. The structure a...In order to cool computer chip efficiently with the least noise, a single phase water-cooling radiator for computer chip driven by piezoelectric pump with two parallel-connection chambers is developed. The structure and work principle of this radiator is described. Material, processing method and design principles of whole radiator are also explained. Finite element analysis （FEA） software, ANSYS, is used to simulate the heat distribution in the radiator. Testing equipments for water-cooling radiator are also listed. By experimental tests, influences of flowrate inside the cooling system and fan on chip cooling are explicated. This water-cooling radiator is proved more efficient than current air-cooling radiator with comparison experiments. During cooling the heater which simulates the working of computer chip with different power, the water-cooling radiator needs shorter time to reach lower steady temperatures than current air-cooling radiator.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
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
文摘In the 9 December 2024 issue of Nature[1],a team of Google engineers reported breakthrough results using“Willow”,their lat-est quantum computing chip(Fig.1).By meeting a milestone“below threshold”reduction in the rate of errors that plague super-conducting circuit-based quantum computing systems(Fig.2),the work moves the field another step towards its promised super-charged applications,albeit likely still many years away.Areas expected to benefit from quantum computing include,among others,drug discovery,materials science,finance,cybersecurity,and machine learning.
基金financially supported by the Ministry of Education(Singapore)(MOE-T2EP50220-0022)SUTD-MIT International Design Center(Singapore)+3 种基金SUTD-ZJU IDEA Grant Program(SUTD-ZJU(VP)201903)SUTD Kickstarter Initiative(SKI 2021_02_03,SKI 2021_02_17,SKI 2021_01_04)Agency of Science,Technology and Research(Singapore)(A20G9b0135)National Supercomputing Centre(Singapore)(15001618)。
文摘Neuromorphic computing has the potential to overcome limitations of traditional silicon technology in machine learning tasks.Recent advancements in large crossbar arrays and silicon-based asynchronous spiking neural networks have led to promising neuromorphic systems.However,developing compact parallel computing technology for integrating artificial neural networks into traditional hardware remains a challenge.Organic computational materials offer affordable,biocompatible neuromorphic devices with exceptional adjustability and energy-efficient switching.Here,the review investigates the advancements made in the development of organic neuromorphic devices.This review explores resistive switching mechanisms such as interface-regulated filament growth,molecular-electronic dynamics,nanowire-confined filament growth,and vacancy-assisted ion migration,while proposing methodologies to enhance state retention and conductance adjustment.The survey examines the challenges faced in implementing low-power neuromorphic computing,e.g.,reducing device size and improving switching time.The review analyses the potential of these materials in adjustable,flexible,and low-power consumption applications,viz.biohybrid spiking circuits interacting with biological systems,systems that respond to specific events,robotics,intelligent agents,neuromorphic computing,neuromorphic bioelectronics,neuroscience,and other applications,and prospects of this technology.
基金supported in part by NSFC under Grant 62422407in part by RGC under Grant 26204424in part by ACCESS–AI Chip Center for Emerging Smart Systems, sponsored by the Inno HK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Government
文摘Robotic computing systems play an important role in enabling intelligent robotic tasks through intelligent algo-rithms and supporting hardware.In recent years,the evolution of robotic algorithms indicates a roadmap from traditional robotics to hierarchical and end-to-end models.This algorithmic advancement poses a critical challenge in achieving balanced system-wide performance.Therefore,algorithm-hardware co-design has emerged as the primary methodology,which ana-lyzes algorithm behaviors on hardware to identify common computational properties.These properties can motivate algo-rithm optimization to reduce computational complexity and hardware innovation from architecture to circuit for high performance and high energy efficiency.We then reviewed recent works on robotic and embodied AI algorithms and computing hard-ware to demonstrate this algorithm-hardware co-design methodology.In the end,we discuss future research opportunities by answering two questions:(1)how to adapt the computing platforms to the rapid evolution of embodied AI algorithms,and(2)how to transform the potential of emerging hardware innovations into end-to-end inference improvements.
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supported in part by the National Key Research and Development Program of China(Grant No.2019YFB2204300)in part by the National Natural Science Foundation of China(Grant Nos.62334008 and 62274154)in part by the Key Program of National Natural Science Foundation of China(Grant No.62134004).
文摘This paper describes a 2D/3D vision chip with integrated sensing and processing capabilities.The 2D/3D vision chip architecture includes a 2D/3D image sensor and a programmable visual processor.In this architecture,we design a novel on-chip processing flow with die-to-die image transmission and low-latency fixed-point image processing.The vision chip achieves real-time end-to-end processing of convolutional neural networks(CNNs)and conventional image processing algo-rithms.Furthermore,an end-to-end 2D/3D vision system is built to exhibit the capacity of the vision chip.The vision system achieves real-timing applications under 2D and 3D scenes,such as human face detection(processing delay 10.2 ms)and depth map reconstruction(processing delay 4.1 ms).The frame rate of image acquisition,image process,and result display is larger than 30 fps.
文摘This paper introduces a new datapath architecture for reconfigurable processors. The proposed datapath is based on Network-on-Chip approach and facilitates tight coupling of all functional units. Reconfigurable functional elements can be dynamically allocated for application specific optimizations, enabling polymorphic computing. Using a modified network simulator, performance of several NoC topologies and parameters are investigated with standard benchmark programs, including fine grain and coarse grain computations. Simulation results highlight the flexibility and scalability of the proposed polymorphic NoC processor for a wide range of application domains.
基金This project is supported by Provincial Natural Science Foundation of Heilongjiang(No.A9809).
文摘A method for determination of tool-chip contact length is theoreticallypresented in orthogonal metal machining. By using computer simulation and based on the analyses ofthe elastro-plastic deformation with lagrangian finite element method in the deformation zone, theaccumulated representative length of the low layer, the tool-chip contact length of the chipcontacting the tool rake are calculated, experimental studies are also carried out with 0.2 percentcarbon steel. It is shown that the tool-chip contact lengths obtained from computer simulation havea good agreement with those of measured values.
文摘Developed a new program structure using in single chip computer system, which based on multitasking mechanism. Discussed the specific method for realization of the new structure. The applied sample is also provided.
基金This project is supported by National Hi-tech Research and Development Program of China (863 Program, No. 2002AA404250)National Natural Science Foundation of China (No. 50575093).
文摘In order to cool computer chip efficiently with the least noise, a single phase water-cooling radiator for computer chip driven by piezoelectric pump with two parallel-connection chambers is developed. The structure and work principle of this radiator is described. Material, processing method and design principles of whole radiator are also explained. Finite element analysis （FEA） software, ANSYS, is used to simulate the heat distribution in the radiator. Testing equipments for water-cooling radiator are also listed. By experimental tests, influences of flowrate inside the cooling system and fan on chip cooling are explicated. This water-cooling radiator is proved more efficient than current air-cooling radiator with comparison experiments. During cooling the heater which simulates the working of computer chip with different power, the water-cooling radiator needs shorter time to reach lower steady temperatures than current air-cooling radiator.
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.
