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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Memristor Crossbar-Based Neuromorphic Computing System: A Case Study
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Memristor Crossbar-Based Neuromorphic Computing System: A Case Study

机译：基于忆阻器交叉开关的神经形态计算系统：一个案例研究

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摘要

By mimicking the highly parallel biological systems, neuromorphic hardware provides the capability of information processing within a compact and energy-efficient platform. However, traditional Von Neumann architecture and the limited signal connections have severely constrained the scalability and performance of such hardware implementations. Recently, many research efforts have been investigated in utilizing the latest discovered memristors in neuromorphic systems due to the similarity of memristors to biological synapses. In this paper, we explore the potential of a memristor crossbar array that functions as an autoassociative memory and apply it to brain-state-in-a-box (BSB) neural networks. Especially, the recall and training functions of a multianswer character recognition process based on the BSB model are studied. The robustness of the BSB circuit is analyzed and evaluated based on extensive Monte Carlo simulations, considering input defects, process variations, and electrical fluctuations. The results show that the hardware-based training scheme proposed in the paper can alleviate and even cancel out the majority of the noise issue.
机译：通过模仿高度并行的生物系统，神经形态硬件提供了在紧凑且节能的平台内进行信息处理的能力。但是，传统的冯·诺依曼架构和有限的信号连接严重限制了此类硬件实现的可伸缩性和性能。最近，由于忆阻器与生物突触的相似性，已经在利用神经形态系统中最新发现的忆阻器方面进行了许多研究。在本文中，我们探索了具有自动联想记忆功能的忆阻器交叉开关阵列的潜力，并将其应用于盒中脑状态（BSB）神经网络。特别是，研究了基于BSB模型的多答案字符识别过程的召回和训练功能。考虑到输入缺陷，工艺变化和电气波动，将基于广泛的蒙特卡洛模拟对BSB电路的鲁棒性进行分析和评估。结果表明，本文提出的基于硬件的训练方案可以缓解甚至消除大部分噪声问题。

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