Market for memristors and data mining memory structures for promising smart systems

Andrey I. Vlasov (), Ivan V. Gudoshnikov (), Vladimir P. Zhalnin (), Aksultan T. Kadyr () and Vadim A. Shakhnov ()
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Andrey I. Vlasov: Bauman Moscow State Technical University, Russian Federation
Ivan V. Gudoshnikov: Bauman Moscow State Technical University, Russian Federation
Vladimir P. Zhalnin: Bauman Moscow State Technical University, Russian Federation
Aksultan T. Kadyr: Bauman Moscow State Technical University, Russian Federation
Vadim A. Shakhnov: Bauman Moscow State Technical University, Russian Federation

Entrepreneurship and Sustainability Issues, 2020, vol. 8, issue 2, 98-115

Abstract: The article examines the market for promising memristor-based memories for smart systems. The implementation of smart systems is characterized by the widespread use cyber-physical systems, predictive maintenance, AR/VR (Augmented/Virtual Reality) technologies, the Internet of Things (IoT), and Machine Learning algorithms. To stimulate their development, an increasing amount of computational resources and new data storage technologies is required. The current study aims to analyze the development of today’s memristive technologies market in the context of their influence on the development of smart systems. The authors discuss the key stages of the market’s formation and assess the possible effect of memristive technology on various spheres of society’s life. The research results show that the application of memristive technology can affect the development dynamics of both data mining and promising data storage systems. The estimates obtained demonstrate that the memristor market is highly competitive and there are a considerable number of active participants operating on it. The majority of companies expand their market presence by entering various end-user segments. The annual market growth rate will average about 80% and reach an estimate of USD 8.9 billion by 2024 and USD 13.5 billion by 2027.

Keywords: memristor market; industry digitalization; data storage; data mining; smart systems (search for similar items in EconPapers)
JEL-codes: D40 M15 O14 O32 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:ssi:jouesi:v:8:y:2020:i:2:p:98-115

DOI: 10.9770/jesi.2020.8.2(6)

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