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
Recent advances in Artificial Intelligence(AI)have indicated that inspirations from the brain can effectively improve the level of intelligence for AI computational models,even if just local and partial inspirations.N...Recent advances in Artificial Intelligence(AI)have indicated that inspirations from the brain can effectively improve the level of intelligence for AI computational models,even if just local and partial inspirations.Nevertheless,realizing and exceeding intelligence at a human level still needs a deeper investigation and inspirations from the brain.The goal of brain-inspired intelligence is to achieve human intelligence inspired from brain neural mechanism and cognitive behavior mechanism.To this end,in this paper we introduce the relationship between AI and neuroscience,the current status of brain-inspired intelligence,the future work in intelligent control systems,and its profound influence in other fields.展开更多
A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuch...A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuchongzhi-3,a superconducting quantum computing prototype featuring 105 qubits and 182 couplers.展开更多
目前,多核实时系统中同步任务的节能调度研究主要针对的是同构多核处理器平台,而异构多核处理器架构能够更有效地发挥系统性能。将现有的研究直接应用于异构多核系统,在保证可调度性的情况下会导致能耗变高。对此,通过使用动态电压与频...目前,多核实时系统中同步任务的节能调度研究主要针对的是同构多核处理器平台,而异构多核处理器架构能够更有效地发挥系统性能。将现有的研究直接应用于异构多核系统,在保证可调度性的情况下会导致能耗变高。对此,通过使用动态电压与频率调节(Dynamic Voltage Frequency Scaling,DVFS)技术,研究异构多核实时系统中基于任务同步的节能调度问题,提出同步感知的最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First,SA-LESF)。该算法针对所有任务的速度配置进行迭代优化,直至所有任务均达到其最大限度节能的速度配置。此外,进一步提出基于动态松弛时间回收的同步感知最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First with Dynamic Reclamation,SA-LESF-DR)。该算法在保证实时任务可调度的同时,实施相应的回收策略,进一步降低系统能耗。实验结果表明,SA-LESF与SA-LESF-DR算法在能耗表现上具有优势,在相同任务集下,相比其他算法可节省高达30%的能耗。展开更多
This paper presents a smart compensation system based on MCA7707 (a kind of signal processor). The li near errors and high order errors of a sensor (especially piezoresistive sensor) can be corrected by using this s...This paper presents a smart compensation system based on MCA7707 (a kind of signal processor). The li near errors and high order errors of a sensor (especially piezoresistive sensor) can be corrected by using this system. It can optimize the process of piezoresi stive sensor calibration and compensation, then, a total error factor within 0.2 % of the sensor′s repeatability errors is obtained. Data are recorded and coeff icients are determined automatically by this system, thus, the sensor compensati on is simplified greatly. For operating easily, a wizard compensation program is designed to correct every error and to get the optimum compensation.展开更多
Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional mult...Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional multi-branch convolutional networks suffer from computational redundancy,high memory access overhead,and inefficient branch fusion.Therefore,this paper proposes an adaptive multi-branch convolutional module(AMBC)that integrates software-hardware co-optimization.During training,the learnable fusion coefficients are introduced to enable adaptive fusion of multi-scale features,while in the inference phase,the multiple branches and their normalization parameters are merged with the fusion coefficients into a single 3×3 convolutional kernel through operator fusion.On the SIREA-288 reconfigurable platform,compared with unoptimized multi-branch networks,the proposed AMBC reduces external memory accesses by 47.91%and inference latency by 47.20%,achieving a 1.90×speedup.This approach maximizes the utilization of the reconfigurable logic while minimizing both reconfiguration and data-movement overheads in edge inference.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supported by the General Program of National Natural Science Foundation of China(Grant No.61876021)the General Program of Beijing Natural Science Foundation(Grant No.4212037)。
文摘Recent advances in Artificial Intelligence(AI)have indicated that inspirations from the brain can effectively improve the level of intelligence for AI computational models,even if just local and partial inspirations.Nevertheless,realizing and exceeding intelligence at a human level still needs a deeper investigation and inspirations from the brain.The goal of brain-inspired intelligence is to achieve human intelligence inspired from brain neural mechanism and cognitive behavior mechanism.To this end,in this paper we introduce the relationship between AI and neuroscience,the current status of brain-inspired intelligence,the future work in intelligent control systems,and its profound influence in other fields.
文摘A team of researchers from the University of Science and Technology of China(USTC)of the Chinese Academy of Sciences(CAS)and its partners have made significant advancements in random quantum circuit sampling with Zuchongzhi-3,a superconducting quantum computing prototype featuring 105 qubits and 182 couplers.
文摘目前,多核实时系统中同步任务的节能调度研究主要针对的是同构多核处理器平台,而异构多核处理器架构能够更有效地发挥系统性能。将现有的研究直接应用于异构多核系统,在保证可调度性的情况下会导致能耗变高。对此,通过使用动态电压与频率调节(Dynamic Voltage Frequency Scaling,DVFS)技术,研究异构多核实时系统中基于任务同步的节能调度问题,提出同步感知的最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First,SA-LESF)。该算法针对所有任务的速度配置进行迭代优化,直至所有任务均达到其最大限度节能的速度配置。此外,进一步提出基于动态松弛时间回收的同步感知最大能耗节省优先算法(Synchronization Aware-Largest Energy Saved First with Dynamic Reclamation,SA-LESF-DR)。该算法在保证实时任务可调度的同时,实施相应的回收策略,进一步降低系统能耗。实验结果表明,SA-LESF与SA-LESF-DR算法在能耗表现上具有优势,在相同任务集下,相比其他算法可节省高达30%的能耗。
文摘This paper presents a smart compensation system based on MCA7707 (a kind of signal processor). The li near errors and high order errors of a sensor (especially piezoresistive sensor) can be corrected by using this system. It can optimize the process of piezoresi stive sensor calibration and compensation, then, a total error factor within 0.2 % of the sensor′s repeatability errors is obtained. Data are recorded and coeff icients are determined automatically by this system, thus, the sensor compensati on is simplified greatly. For operating easily, a wizard compensation program is designed to correct every error and to get the optimum compensation.
文摘仿真点(simulation point,SimPoint)作为一种代表性采样技术被广泛应用于处理器硅前性能评估中。SimPoint为每个待评估的程序根据贝叶斯信息准则确定仿真点数目。然而,标准测试集内不同程序有着不同的行为复杂程度,需要不同数目的仿真点来准确刻画其程序行为。SimPoint无法识别出不同程序间的复杂度差异,无法做到在总仿真点数目一定的情况下,将更多的仿真点分配给行为复杂的程序以降低这些程序的性能评估误差,将更少的仿真点分配给行为简单的程序而不损失这些程序的性能评估精度。由于没有在测试集内合理地进行仿真点分配,SimPoint虽然可以给出比较准确的平均性能评估误差,但是某些行为复杂的测试子项的性能评估误差依然较大。针对这一问题,本文优化了SimPoint的仿真点局部分配方式,提出了一种全局贪心分配方法———贪心点(greedy point,GreedyPoint)方法。该方法将仿真点的分配问题抽象为含约束的优化问题,使用微架构无关特征计算表征误差,通过全局贪心算法来求解该优化问题。实验数据表明,在相同仿真开销下,与SimPoint相比,GreedyPoint可以将SPEC CPU 2017测试套件的平均性能评估误差由3.23%降低到2.08%,最大性能评估误差由21.22%大幅降低至7.01%。
基金Supported by the National Science and Technology Major Project of China(2022ZD0119005)the Natural Science Project of Shaanxi Province(2025JC-YBMS-754,2024JC-YBMS-539)。
文摘Reconfigurable array architecture has become an important hardware platform for edge-side deployment of convolutional neural networks due to their high parallelism and flexible programmability.However,traditional multi-branch convolutional networks suffer from computational redundancy,high memory access overhead,and inefficient branch fusion.Therefore,this paper proposes an adaptive multi-branch convolutional module(AMBC)that integrates software-hardware co-optimization.During training,the learnable fusion coefficients are introduced to enable adaptive fusion of multi-scale features,while in the inference phase,the multiple branches and their normalization parameters are merged with the fusion coefficients into a single 3×3 convolutional kernel through operator fusion.On the SIREA-288 reconfigurable platform,compared with unoptimized multi-branch networks,the proposed AMBC reduces external memory accesses by 47.91%and inference latency by 47.20%,achieving a 1.90×speedup.This approach maximizes the utilization of the reconfigurable logic while minimizing both reconfiguration and data-movement overheads in edge inference.
