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Neuromorphic Computing Based on Emerging Memory Technologies
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DOI：10.1109/JETCAS.2016.2533298.png)
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Abstract
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In this paper, we review some of the novel emerging memory technologies and how they can enable energy-efficient implementation of large neuromorphic computing systems. We will highlight some of the key aspects of biological computation that are being mimicked in these novel nanoscale devices, and discuss various strategies employed to implement them efficiently. Though large scale learning systems have not been implemented using these devices yet, we will discuss the ideal specifications and metrics to be satisfied by these devices based on theoretical estimations and simulations. We also outline the emerging trends and challenges in the path towards successful implementations of large learning systems that could be ubiquitously deployed for a wide variety of cognitive computing tasks.
Keywords:
Cognitive computing
memristor
neuromorphic engineering
phase change memory (PCM)
resistive random-access memory (RRAM)
spin-transfer torque random-access memory (STT-RAM)
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