FMI’s New Report, titled, “Neuromorphic Chip Market: Global Industry Analysis and Opportunity Assessment 2016-2026” offers 10-year forecast and analysis.
Valley Cottage, NY -- (SBWIRE) -- 05/30/2016 -- Neuromorphic chips are inspired from functionality of human brain. Neuromorphic chips are arrangement of transistors installed over a silicon fabrication chip, which helps in reducing memory consumption and power consumption. They are designed in such a manner to create the artificial neural network out of transistor, thus imitating the human brain.
Neuromorphic Chip Market: Drivers and Restraint
Over the time, demand for neuromorphic chip is expected to grow significantly over the forecast period. Factors which are driving the growth of global neuromorphic chip market are development in the field of data analytics, growth of artificial intelligence and machine learning, miniaturization of integrated circuits, growing sensor market.
On the other hand, factors which are restraining the growth of global neuromorphic chip market are slow growth rate of commercialization of applications due to lack of research & development and investment. However, in future innovative applications in the field of automation is expected to create great growth opportunity for the global neuromorphic chip market over the forecast period.
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Neuromorphic Chip Market: Segmentation
Global neuromorphic chip market is segmented on the basis of application, end-user industry and region. On the basis of application, the global neuromorphic chip market can be segmented into speech recognition, signal recognition, image recognition and data mining. Out of these, image recognition application segment is expected to hold highest share of the global neuromorphic chip market by 2016 and it is expected dominate the market throughout the forecast period. However, signal recognition application segment is expected to witness a highest compound annual growth rate over the forecast period of 2016-2026.
On the basis of end-user industry, the global neuromorphic chip market can be segmented into consumer industry, medical & healthcare, aerospace, military & defence, industrial, automotive and others. Consumer industry is expected to hold major share of the global neuromorphic chip market by 2016 end and expected to remain dominant throughout the forecast period. Aerospace, military & defence industry is the second largest end-user segment followed by consumer industry. However, remaining industries are expected to adopt neuromorphic chip by 2018.
Neuromorphic Chip Market: Region wise outlook
On the basis of region, the global neuromorphic chip market can be segmented into seven regions which include – North America, Latin America, Asia-Pacific excluding Japan (APEJ), Western Europe, Eastern Europe, Middle East and Africa (MEA) and Japan. Further the market is sub-segmented as per the major countries of each region in order to provide better regional analysis of neuromorphic chip market. Out of all these region, Asia-Pacific (APEJ) is expected to hold the largest share of global neuromorphic chip market by 2016 end. However, North America is expected to witness a highest CAGR over the period of forecast. As per our analysis, globally China, South Korea, Germany and U.S. are estimated to be the major markets for neuromorphic chips and they are expected to witness highest compound annual growth rate.
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Neuromorphic Chip Market: Key Players
Key players in the global neuromorphic chip market are IBM Corporation, Qualcomm Technologies, Inc. and Intel Corporation. These key players are investing heavily on R&D activity in order to develop the superior technology. Apart from them few other players are also included in the report. Key market developments and strategies of market players are also included. For example, In June 2012, Intel Corporation unveils design of its neuromorphic chip. In October 2013, Qualcomm Technologies, Inc. introduced "Qualcomm Zeroth Processor" – a brain inspired computing. In August 2015, IBM created neuromorphic chip which is as powerful as a rodent's brain, this system is designed to understand deep-learning algorithms, consumes less electricity and compact in size.