Hybrid neuromorphic computing,integrating Artificial Neural Networks(ANNs)and Spiking Neural Networks(SNNs),is a key approach to advancing Artificial General Intelligence(AGI).Current hybrid platforms are limited to L...Hybrid neuromorphic computing,integrating Artificial Neural Networks(ANNs)and Spiking Neural Networks(SNNs),is a key approach to advancing Artificial General Intelligence(AGI).Current hybrid platforms are limited to Leaky Integrate-and-Fire(LIF)based SNNs,missing crucial biological neuron behaviors like bursting and adaptation.We propose a hybrid platform based on the TianjicX chip,enabling heterogeneous integration of multiple SNN models(LIF,Quadratic Integrate-and-Fire(QIF),and Izhikevich)alongside ANNs.Our platform employs a co-design strategy for computing and storage mechanisms,minimizing data movement.Simulations show that the co-design approach reduces energy consumption by 8.11%(48.67 mW)compared to TianjicX.The platform also demonstrates superior computational performance across SNN models.It achieves 95%classification accuracy on the MNIST dataset(3000 images,each being 28 pixel×28 pixel and single presentation),surpassing Open Date Index Name(ODIN)by 10.5%.This is achieved with a two-layer fully-connected Izhikevich network(784×800×10),where each synapse operates at 8-bit precision.The network processes 33900 images per second,using only 35 cores(21.88%of 160 cores)and delivering 896 billion operations per second.Furthermore,on ResNet-50,our platform shows a 3.12%increase in computing speed and 40.85 mW/frame reduction in energy consumption compared to the TianjicX chip.展开更多
由于传统的互补金属-氧化物-半导体(Complementary Metal Oxide Semiconductor,CMOS)神经元电路与生物学的契合性较差且电路复杂,提出了一种基于忆阻器的多端口输入的泄露-整合-激发(Leaky-Integrate-Fire,LIF)神经元电路。该电路由运...由于传统的互补金属-氧化物-半导体(Complementary Metal Oxide Semiconductor,CMOS)神经元电路与生物学的契合性较差且电路复杂,提出了一种基于忆阻器的多端口输入的泄露-整合-激发(Leaky-Integrate-Fire,LIF)神经元电路。该电路由运放、逻辑门等器件以及忆阻器构成,主要分为信号叠加模块和神经元信号产生模块。通过施加多个双尖峰脉冲信号并调节输入信号的数量和频率,模拟了生物神经元受到的不同程度刺激。研究发现施加到神经元上信号的数量和频率达到一定的值,神经元电路才会输出电压信号,这与生物体中只有受到一定程度的刺激时才会做出反应的现象是一致的。进一步,调节该电路中神经元信号产生模块的阈值电压大小,研究发现输入相同的信号,只有当电路的阈值电压较低时,神经元电路才能输出电压信号,这与生物中不同部位受到相同的刺激,神经元兴奋程度越高,越容易做出反应的现象一致。由此,该文所提出的LIF神经元电路不仅解决了传统电路输入信号单一、输入信号波形与生物信号波形差异大等问题,而且能模拟生物神经元的兴奋程度,这为人工神经网络的设计提供理论依据。展开更多
文摘Hybrid neuromorphic computing,integrating Artificial Neural Networks(ANNs)and Spiking Neural Networks(SNNs),is a key approach to advancing Artificial General Intelligence(AGI).Current hybrid platforms are limited to Leaky Integrate-and-Fire(LIF)based SNNs,missing crucial biological neuron behaviors like bursting and adaptation.We propose a hybrid platform based on the TianjicX chip,enabling heterogeneous integration of multiple SNN models(LIF,Quadratic Integrate-and-Fire(QIF),and Izhikevich)alongside ANNs.Our platform employs a co-design strategy for computing and storage mechanisms,minimizing data movement.Simulations show that the co-design approach reduces energy consumption by 8.11%(48.67 mW)compared to TianjicX.The platform also demonstrates superior computational performance across SNN models.It achieves 95%classification accuracy on the MNIST dataset(3000 images,each being 28 pixel×28 pixel and single presentation),surpassing Open Date Index Name(ODIN)by 10.5%.This is achieved with a two-layer fully-connected Izhikevich network(784×800×10),where each synapse operates at 8-bit precision.The network processes 33900 images per second,using only 35 cores(21.88%of 160 cores)and delivering 896 billion operations per second.Furthermore,on ResNet-50,our platform shows a 3.12%increase in computing speed and 40.85 mW/frame reduction in energy consumption compared to the TianjicX chip.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(Brain Science and Brain-Like Intelligence Technology,No.2025ZD0214902)the National Natural Science Foundation of China(No.32271047,32471054)。
文摘由于传统的互补金属-氧化物-半导体(Complementary Metal Oxide Semiconductor,CMOS)神经元电路与生物学的契合性较差且电路复杂,提出了一种基于忆阻器的多端口输入的泄露-整合-激发(Leaky-Integrate-Fire,LIF)神经元电路。该电路由运放、逻辑门等器件以及忆阻器构成,主要分为信号叠加模块和神经元信号产生模块。通过施加多个双尖峰脉冲信号并调节输入信号的数量和频率,模拟了生物神经元受到的不同程度刺激。研究发现施加到神经元上信号的数量和频率达到一定的值,神经元电路才会输出电压信号,这与生物体中只有受到一定程度的刺激时才会做出反应的现象是一致的。进一步,调节该电路中神经元信号产生模块的阈值电压大小,研究发现输入相同的信号,只有当电路的阈值电压较低时,神经元电路才能输出电压信号,这与生物中不同部位受到相同的刺激,神经元兴奋程度越高,越容易做出反应的现象一致。由此,该文所提出的LIF神经元电路不仅解决了传统电路输入信号单一、输入信号波形与生物信号波形差异大等问题,而且能模拟生物神经元的兴奋程度,这为人工神经网络的设计提供理论依据。
