The neuromorphic computing is executed on hardware by threshold switches, transistors and oxide based memristors. Neuromorphic chips are the amalgamation of memristors and transistors deployed over a silicon fabrication chip, which assists to lessen memory consumption in real time manner. Moreover, neuromorphic chips are the digital and analog very large scale integration (VLSI) system which performs as neural systems models. Implementation of neuromorphic chip is likely to increase scalability, performance and sensitivity of machines.
The neuromorphic chip market is driven primarily due to the increasing demand of artificial intelligence. Artificial intelligence is a machine learning technology that provides the skill to machines to learn with partial programming. This involves the advancement of computer programs and machines which are competent enough to update themselves when being exposed to real time data. Moreover, an innovation in the field of miniaturization of integrated circuits has increased the scope of applications for neuromorphic chip. The neuromorphic chips are small and scalable enough to get easily implemented in different end use products. Rising demand of neuromorphic chip in the field of Internet of Things (IoT) technology is driving the growth of the market. The chips are likely being planted in large supercomputers to enhance the speed of machine learning along with some other neural network-based computations.
Growth of neuromorphic chip market is hindered due to high cost associated in manufacturing of such chips. Manufacturer faces different tribulations in integrating biological synapses in to a minute hardware which requires just a single micron of space. Complexities in hardware designing are also major restraining factors in this market. The complicated neuromorphic synapses are difficult to implement in hardware. As of 2015, in developing countries, developments in the field of neuromorphic computing have negatively affected due to lack of availability of technological resources and lack of availability of competent infrastructure.
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Implementation of neuromorphic chips in diverse end use products of different industries including automotive, semiconductor & electronics and healthcare can be identified as future growth opportunity for the market. Neuromorphic chips have application level opportunities in industrial and service robotics.
Based on function, the market is segregated into signal processing, data processing, image recognition and others. On the basis of application, the neuromorphic chips market is segmented into defense and aerospace, automotive, medical, industrial and others. On the basis of geography, the market report is segregated into North America, Europe, Asia Pacific and Rest of the World (RoW). The scope of the report offers an insight into neuromorphic chips market in these regions based on revenue (USD million).
The competitive profiling of the key players in the global neuromorphic chips market across four broad geographic regions is included in the study. These include different business strategies adopted by the leading players and their recent developments as in the field of neuromorphic chips. The market attractive analysis of the major application areas has been provided in the report, in order to offer a deep insight of global neuromorphic chips market.
A comprehensive analysis of the market dynamics that is inclusive of market drivers, restraints and opportunities is included in the purview of the report. Market dynamics are the distinctive factors which impact the market growth, thereby helping to understand the ongoing trends of the global market. Therefore, the report provides the forecast of the global market for the period from 2015 to 2023, along with offering an inclusive study of the neuromorphic chips market.