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arXiv · 1108.4182

Memristor-based Synaptic Networks and Logical Operations Using In-Situ Computing

Omid Kavehei·Said Al-Sarawi·Kyoung-Rok Cho·Nicolangelo Iannella·Sung-Jin Kim·Kamran Eshraghian·Derek Abbott
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Abstract

We present new computational building blocks based on memristive devices. These blocks, can be used to implement either supervised or unsupervised learning modules. This is achieved using a crosspoint architecture which is an efficient array implementation for nanoscale two-terminal memristive devices. Based on these blocks and an experimentally verified SPICE macromodel for the memristor, we demonstrate that firstly, the Spike-Timing-Dependent Plasticity (STDP) can be implemented by a single memristor device and secondly, a memristor-based competitive Hebbian learning through STDP using a $1\times 1000$ synaptic network. This is achieved by adjusting the memristor's conductance values (weights) as a function of the timing difference between presynaptic and postsynaptic spikes. These implementations have a number of shortcomings due to the memristor's characteristics such as memory decay, highly nonlinear switching behaviour as a function of applied voltage/current, and functional uniformity. These shortcomings can be addressed by utilising a mixed gates that can be used in conjunction with the analogue behaviour for biomimetic computation. The digital implementations in this paper use in-situ computational capability of the memristor.

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Omid Kavehei, Said Al-Sarawi, Kyoung-Rok Cho, Nicolangelo Iannella, Sung-Jin Kim, Kamran Eshraghian, Derek Abbott. 2011-08-21. Memristor-based Synaptic Networks and Logical Operations Using In-Situ Computing. https://arxiv.org/abs/1108.4182

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