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arXiv 1810.03333cs.ET
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基于忆阻器的阈值逻辑门的实际实现

Practical Implementation of Memristor-Based Threshold Logic Gates

Georgios Papandroulidakis, Alexantrou Serb, Ali Khiat, Geoff V. Merrett, Themistoklis Prodromakis

摘要:

新兴存储器技术的当前进步使得能够实现用于高性能和低功率电子系统的新颖和非常规计算架构,其能够在边缘处执行大规模并行操作。一种新兴技术,ReRAM,也已知属于忆阻器(记忆电阻器)家族,由于其用于逻辑和存内计算的有吸引力的特征而引起关注;其技术属性带来的好处,例如纳米尺度、低功率操作和多态编程。与此同时,CMOS设计正在迅速达到其物理和功能限制,并进一步研究了新的逻辑家族,如阈值逻辑门(TLG)。TLG构成了以其高速和低功耗而闻名的逻辑家族,但依赖于传统的晶体管技术。忆阻器的引入使得TLG的重新配置能力更经济实惠。通过这项工作,我们介绍了一个基于忆阻器的电流模式TLG(MCMTLG)电路的物理实现,并通过多个实验装置验证其设计和操作。我们展示了2输入和3输入MCMTLG配置,并展示了他们的重新配置能力。这是通过任意改变忆阻权重来实现的,用于形成分类决策边界,从而显示出作为人工神经网络(ANN)的替代硬件友好实现的前景。通过采用真实的忆阻器设备作为TLG中的突触权重的等效物,我们正在实现可以用于计算机分类器的组件。

英文摘要:

Current advances in emerging memory technologies enable novel and unconventional computing architectures for high-performance and low-power electronic systems, capable of carrying out massively parallel operations at the edge. One emerging technology, ReRAM, also known to belong in the family of memristors (memory resistors), is gathering attention due to its attractive features for logic and in-memory computing; benefits which follow from its technological attributes, such as nanoscale dimensions, low power operation and multi-state programming. At the same time, design with CMOS is quickly reaching its physical and functional limitations, and further research towards novel logic families, such as Threshold Logic Gates (TLGs) is scoped. TLGs constitute a logic family known for its high-speed and low power consumption, yet rely on conventional transistor technology. Introducing memristors enables a more affordable reconfiguration capability of TLGs. Through this work, we are introducing a physical implementation of a memristor-based current-mode TLG (MCMTLG) circuit and validate its design and operation through multiple experimental setups. We demonstrate 2-input and 3-input MCMTLG configurations and showcase their reconfiguration capability. This is achieved by varying memristive weights arbitrarily for shaping the classification decision boundary, thus showing promise as an alternative hardware-friendly implementation of Artificial Neural Networks (ANNs). Through the employment of real memristor devices as the equivalent of synaptic weights in TLGs, we are realizing components that can be used towards an in-silico classifier.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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