All-memristive neuromorphic computing with level-tuned neurons

神经形态工程学 记忆电阻器 冯·诺依曼建筑 计算机科学 计算机体系结构 人工神经网络 尖峰神经网络 过程(计算) 油藏计算 电阻随机存取存储器 电子线路 人工智能 CMOS芯片 生物神经网络 Spike(软件开发) 分布式计算 电子工程 循环神经网络 工程类 软件工程 操作系统
作者
Angeliki Pantazi,Stanisław Woźniak,Tomáš Tůma,Evangelos Eleftheriou
出处
期刊:Nanotechnology [IOP Publishing]
卷期号:27 (35): 355205-355205 被引量:101
标识
DOI:10.1088/0957-4484/27/35/355205
摘要

In the new era of cognitive computing, systems will be able to learn and interact with the environment in ways that will drastically enhance the capabilities of current processors, especially in extracting knowledge from vast amount of data obtained from many sources. Brain-inspired neuromorphic computing systems increasingly attract research interest as an alternative to the classical von Neumann processor architecture, mainly because of the coexistence of memory and processing units. In these systems, the basic components are neurons interconnected by synapses. The neurons, based on their nonlinear dynamics, generate spikes that provide the main communication mechanism. The computational tasks are distributed across the neural network, where synapses implement both the memory and the computational units, by means of learning mechanisms such as spike-timing-dependent plasticity. In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors.
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