Neuromorphic Chip Market (By Function - Signal Processing, Data Processing, Image Recognition and Others, By Application - Defense and Aerospace, Automotive, Medical, Industrial and Others) - Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2015 - 2023
Albany, NY -- (SBWIRE) -- 10/03/2017 -- The global neuromorphic chip market is expected to grow considerably during the forecast period from 2015 to 2023. The global market for neuromorphic chips is estimated reach an overall valuation of US$1.809 bn by the end of the given forecast period by registering a robust CAGR of 19.1% in the forecast period.
Geographically, North America dominated the global neuromorphic chip market in the recent past and is expected to continue its dominance in the coming years too. This is because of the increasing use of these neuromorphic chips in the defense and aerospace sectors. Also, increasing use of these chips to power the Internet of Things (IoT) is boosting the market as well. Furthermore, implementation of these neuromorphic chips in the signal processing segment has been projected to witness great development in the coming years.
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Defense, aerospace, automotive, and logistics are some of the segments that heavily make use of the neuromorphic chips for image recognitions. Increasing demand for artificial intelligence is also one of the significant drivers of growth in the global neuromorphic chip market. Artificial intelligence is a type of machine learning technology that enables machines or computers to cope with partial programming. It involves use of advanced computing programs which are potent enough to update themselves when exposed to real time information or data.
The artificial intelligence enforces these neuromorphic chips to carry out numerous operation in different ways. It leads to the conclusion that the artificial intelligence possibly will perform a specific task in multiple ways instead of sticking to one single way of completing that task. Thus, evolution in artificial intelligence is also projected to bring about a huge development in the global neuromorphic chips over the course of the given forecast period.
On the flipside, there are many complexities involved in designing the hardware of neuromorphic chips. The neuromorphic synapses are highly complicated and are difficult to implement in the established chip design hardware. The reason for this difficulty is the elaborate algorithm and space required to be run in the given structured hardware. Another big challenge is the restriction on the capacity of memory that can be incorporated in these neuromorphic chips. In the coming years, with more technological advancements, these complexities are expected to diminish.
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This review is based on the research report published by Transparency Market Research titled "Neuromorphic Chip Market (Function - Signal Processing, Data Processing, and Image Recognition; Application - Defense and Aerospace, Automotive, Medical, and Industrial) - Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2015 - 2023
Some of the key vendors in the global neuromorphic chip market are Brain Corporation, Qualcomm Inc., Intel Corp., International Business Machine Corporation, Vicarious FPC Inc., HRL Laboratories LLC., Hewlett Packard Corp., General Vision Inc., Samsung Electronics Co. Ltd., and Lockheed Martin Corporation. The global market for neuromorphic chips is highly fragmented. The prominent companies in the market are taking strategic decisions such as mergers and acquisitions to enhance their market presence.
These companies are also raising funds to grow their operations and market shares. Recently, artificial intelligence start-up company Vicarious raised US$50 million. Established in 2010, Vicarious is now developing the 'recursive cortical network', a working system that the start-up claims use the theoretical computational principles of the human brain to construct a software that can learn and think just like a human.
The network works a visual impression system that can decode the content of videos and photographs in a way similar to humans by making use of mathematics, sensory data, and biological plausibility.