Perspective: A review on memristive hardware for neuromorphic computation

Neuromorphic computation is one of the axes of parallel distributed processing, and memristor-based synaptic weight is considered as a key component of this type of computation. However, the material properties of memristors, including material related physics, are not yet matured. In parallel with...

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Bibliographic Details
Published in:Journal of applied physics 2018-10, Vol.124 (15)
Main Authors: Sung, Changhyuck, Hwang, Hyunsang, Yoo, In Kyeong
Format: Article
Language:English
Subjects:
Application specific integrated circuits
Applied physics
Artificial intelligence
CMOS
Computation
Distributed processing
Field programmable gate arrays
Integrated circuits
Material properties
Memristors
Microprocessors
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Summary:Neuromorphic computation is one of the axes of parallel distributed processing, and memristor-based synaptic weight is considered as a key component of this type of computation. However, the material properties of memristors, including material related physics, are not yet matured. In parallel with memristors, CMOS based Graphics Processing Unit, Field Programmable Gate Array, and Application Specific Integrated Circuit are also being developed as dedicated artificial intelligence (AI) chips for fast computation. Therefore, it is necessary to analyze the competitiveness of the memristor-based neuromorphic device in order to position the memristor in the appropriate position of the future AI ecosystem. In this article, the status of memristor-based neuromorphic computation was analyzed on the basis of papers and patents to identify the competitiveness of the memristor properties by reviewing industrial trends and academic pursuits. In addition, material issues and challenges are discussed for implementing the memristor-based neural processor.
ISSN:0021-8979
1089-7550
DOI:10.1063/1.5037835