The neuromorphic computing market is expected to be valued at USD 22,743 thousand in 2021 and is expected to reach USD 550,593 thousand by 2026, at a CAGR of 89.1% between 2021 and 2026. The need for better performing ICs, increase in demand for artificial intelligence and machine learning, and increasing number of cross-industry partnerships and collaborations are key factors driving the growth of the market. Artificial intelligence (AI) is being adopted in industries such as medical, media, entertainment, telecom, utility, aerospace, military, consumer devices, food & beverages, and piping. A combination of AI systems and machine learning is set to revolutionize the business environment with smart decisions. However, lack of knowledge about neuromorphic computing and complex algorithms increasing complexity of designing hardware of neuromorphic chips can act as a major challenges in the market during the forecast period.
The neuromorphic computing market was dominated by Intel Corp. (US), IBM Corporation (US), BrainChip Holdings Ltd. (US), Qualcomm (US) and HP Enterprise (US). The major strategies adopted by the top 5 players in the neuromorphic computing market included product launches and developments, collaborations, and acquisitions which helped them to innovate on their product offerings and broaden their customer base.
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Top 2 players in the neuromorphic computing are:
Intel Corporation ranked first in the neuromorphic computing market in 2020. Intel, in collaboration with Purdue University, started to work on the designing of neuromorphic chip. In 2012, Intel revealed its spin-based neuromorphic microchips as the ultimate parallel processors and was planning to progress on system-level modelling of large-scale neuromorphic architectures based on the proposed device–circuit scheme. In June 2019, the company announced, that an 8 million-neuron neuromorphic system comprising 64 Loihi research chips is available to the broader research community. With Pohoiki Beach, researchers can experiment with Intel’s brain-inspired research chip, Loihi, which applies the principles found in biological brains to computer architectures.
Loihi enables users to process information up to 1,000 times faster and 10,000 times more efficiently than CPUs for specialized applications like sparse coding, graph search and constraint satisfaction problems. For the development of neuromorphic chip, Intel joined hands with AMD (US), one of the leading technology development companies, and this partnership is likely to help Intel make use of new innovative solutions for neuromorphic chip development. Intel has also started a Parallel Computing Lab which focuses on applications such as big data, machine learning, neuromorphic computing, extreme-scale computing, multimodal real-time physical simulation, behavioral simulation, interventional medical imaging, large-scale optimization (FSI), and computational biology.
IBM Corporation ranked second in the neuromorphic computing market in 2020. IBM is a pioneer in neuromorphic chip design and has a good brand recognition. It unveiled the first brain-like neuromorphic chip back in 2011. The company tends to make successful acquisitions for expansion of its reach as well as business operations. For instance, the company is planning to complement Watson cognitive computing skills with speed and pattern recognition capabilities of neuromorphic chips. IBM’s goal is to give every business professional access to advanced cognitive-powered predictive analytics, coupled with new forms of data, In 2011, IBM (US) unveiled what it calls TrueNorth, a custom-made, brain-like chip that builds on a simpler experimental system. TrueNorth is equipped with 4,096 processor cores, and it replicates 1 million human neurons and 256 million synapses—two of the fundamental biological building blocks that make up the human brain. It is the largest chip IBM has ever built at 5.4 billion transistors and has an on-chip network of 4,096 neurosynaptic cores.
In January 2021, IBM launched an energy-efficient AI chip built with 7nm technology. The AI hardware accelerator chip supports a variety of model types while achieving leading-edge power efficiency. The chip technology can be scaled and used for commercial applications to train large-scale models in the cloud to security and privacy efforts by bringing training closer to the edge and data closer to the source.
Neuromorphic Computing Market With Covid-19 Impact by Offering, Deployment, Application (Image Recognition, Signal Recognition, Data Mining), Vertical (Aerospace, Military, & Defense, Automotive, Medical) and Geography - Global Forecast to 2026
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