A Spiking Neuromorphic Architecture Using Gated-RRAM for Associative Memory
A Spiking Neuromorphic Architecture Using Gated-RRAM for Associative Memory
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DOI：
10.1145/3461667
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Alexander Jones;Aaron Ruen;R. Jha
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文献类型：
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作者：
Alexander Jones;Aaron Ruen;R. Jha
这项工作报告了一个尖峰neuromorphic架构的联想记忆模拟SPICE环境中使用最近报道的门控RRAM（电阻随机存取存储器）设备作为突触旁边的神经元的基础上互补金属氧化物半导体（CMOS）。该网络利用Verilog A模型来捕获架构内的门控RRAM器件的行为。该模型使用从实验门控RRAM设备，在这项工作中制造和测试获得的参数。使用这些设备串联CMOS神经元电路，我们的研究结果表明，所提出的架构可以学习的关联在真实的时间和检索学习的关联时，不完整的信息提供。这些结果表明，门控RRAM设备的联想记忆任务的尖峰神经形态架构框架内的承诺。
This work reports a spiking neuromorphic architecture for associative memory simulated in a SPICE environment using recently reported gated-RRAM (resistive random-access memory) devices as synapses alongside neurons based on complementary metal-oxide semiconductors (CMOSs). The network utilizes a Verilog A model to capture the behavior of the gated-RRAM devices within the architecture. The model uses parameters obtained from experimental gated-RRAM devices that were fabricated and tested in this work. Using these devices in tandem with CMOS neuron circuitry, our results indicate that the proposed architecture can learn an association in real time and retrieve the learned association when incomplete information is provided. These results show the promise for gated-RRAM devices for associative memory tasks within a spiking neuromorphic architecture framework.