利用比较器亚稳态实现基于非易失性记忆的脉冲神经网络的STDP学习

Sang-gyun Gi, Injune Yeo, Byung-geun Lee
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引用次数: 0

摘要

提出了一种基于非易失性记忆(NVM)的尖峰神经网络(SNN)的尖峰时序相关可塑性学习电路。与实现STDP学习的传统硬件不同,所提出的电路不需要额外的存储器、放大器或STDP尖峰发生器。相反,电路利用动态比较器的比较时间信息来实现STDP学习的非线性传递曲线。该电路包括一个动态比较器、NVM器件和一些根据STDP学习规则编写NVM电导的数字电路。最后,利用电导响应模型和设计的STDP学习电路,将STDP的仿真结果与数学STDP进行了比较。所提出的电路应用于基于nvm的SNN硬件或其他仿生硬件系统的设计。
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Implementation of STDP Learning for Non-volatile Memory-based Spiking Neural Network using Comparator Metastability
This paper presents a circuit for spike-timing dependent plasticity (STDP) learning of a non-volatile memory (NVM) based spiking neural network (SNN). Unlike conventional hardware for implementation of STDP learning, the proposed circuit does not require additional memory, amplifiers, or an STDP spike generator. Instead, the circuit utilizes the comparison time information of the dynamic comparator to implement a non-linear transfer curve of STDP learning. The circuit includes a dynamic comparator, NVM device, and some digital circuitry to write the conductance of NVM according to the STDP learning rule. Finally, the conductance response model and designed circuit for the STDP learning are used to compare the simulation results of STDP with mathematical STDP. Applications of the proposed circuit are in the design of NVM-based SNN hardware or other bio-inspired hardware systems.
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