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Neuromorphic Engineering: Will machines now perform human-like tasks?

Published On: October 2022

In the process of computer engineering known as neuromorphic computing, a computer's components are patterned after a human's brain and nervous system. The phrase describes the creation of both software and hardware components in computers.
The two main objectives of neuromorphic computing (sometimes called neuromorphic engineering) are to develop a machine called a cognition machine capable of learning, remembering information, and even drawing logical conclusions. The second objective is to learn new knowledge about how the human brain functions, possibly to validate a rational theory.
The market for neuromorphic computing is expected to reach USD 648.40 million by 2025, growing at an astounding 49.92% CAGR during the forecast period. Over the forecast period, demand for machine learning and artificial intelligence is anticipated to drive market expansion. Robotics, nonlinear controls, translation, chatbots, image processing, computer vision, and language processing all employ AI in some capacity.
The main factors propelling market expansion are the rapidly rising demand for AI and machine learning in various sectors, including media and entertainment, aerospace, and the military, and the increased demand for cognitive and brain robots.
Due to the existence of crucial neuromorphic chip makers in the region, North America currently controls the majority of the neuromorphic computing market. Because of the expansion and the considerable expenditures in neuromorphic projects, Europe is predicted to have tremendous growth from 2022 to 2029.
Various neuromorphic computing applications include object identification, data mining, signal recognition, and picture recognition. During the forecast period, the market players will have lucrative prospects due to the adoption of neuromorphic computing for security purposes and the design and development of neuromorphic chips for brain-based robots and cognitive robots. The market is expanding due to the increasing demand for general-purpose humanoid robots with cognitive and cerebral capabilities. The switch from Von Neumann architecture to neuromorphic chips, another market growth driver, is driven by the inherent technological advantages of neuromorphic chips, such as reduced power consumption, higher speed, and optimal memory usage. The global demand for process automation raised by COVID-19 has contributed to the expansion of the neuromorphic imaging industry in the IT and medical sectors.
On the other hand, it is anticipated that the development of the market would be hampered by sophisticated algorithms that make creating the hardware of neuromorphic chips more difficult and by a lack of awareness about neuromorphic computing. The market for neuromorphic computing is expected to face challenges from applications that depend on software compatibility with neural hardware throughout the forecast period.
Because of their tiny size and low power consumption, experts expect that neuromorphic computers will work well for running AI algorithms at the edge rather than in the cloud when they realize their full potential. Like humans, they would be able to adjust to their environment, remember what is important, and seek out additional knowledge from an outside source (in this example, the cloud) as needed.
Other potential uses for this technology in consumer and business technologies include driverless cars, smart homes, natural language processing, data analytics, process improvement, and real-time image processing for use in law enforcement cameras.
With the impending end of Moore's Law, neuromorphic computing has drawn much attention from big chip manufacturers like IBM and Intel. Intel launched Loihi in 2017. It promises to go around standard designs and attain dramatically new levels of efficiency. So, with such high demands and continuous improvements, the market size tends to increase.

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