Neuromorphic Computing Market 2023 Opportunities, Key Players, Competitive and Regional Analysis

Neuromorphic Computing Market Analysis

The anamorphic 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, increased demand for artificial intelligence and machine learning, and increased cross-industry partnerships and collaborations are key factors driving the market's growth. Artificial intelligence (AI) is being adopted in medical, media, entertainment, telecom, utility, aerospace, military, consumer devices, food & beverages, and piping industries. 

A combination of AI systems and machine learning is set to revolutionize the business environment with smart decisions. However, lack of knowledge about xeromorphic computing and complex algorithms increasing the complexity of designing hardware of endomorphic chips can act as a major challenge in the market during the forecast period.

Intel dominated the anamorphic computing market 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.

The top 2 players in neuromorphic computing are:

Intel Corporation ranked first in the anamorphic computing market in 2020. Intel, in collaboration with Purdue University, started to work on the design of a neurotrophic chip. In 2012, Intel revealed its spin-based trimorphic microchips as the ultimate parallel processors and was planning to progress on system-level modeling of large-scale xeromorphic architectures based on the proposed device–circuit scheme. In June 2019, the company announced that an 8 million-neuron anamorphic 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 innovative solutions for neurotrophic 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.

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IBM Corporation

IBM Corporation ranked second in the endomorphic computing market in 2020. IBM is a pioneer in neurotrophic chip design and has a good brand recognition. It unveiled the first brain-like neurotrophic chip back in 2011. The company tends to make successful acquisitions to expand its reach and business operations. For instance, the company is planning to complement Watson cognitive computing skills with speed and pattern recognition capabilities of neurotrophic 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 Drenthe, a custom-made, brain-like chip that builds on a simpler experimental system. True north 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 parasynaptic cores.

Recent Developments

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.

1 INTRODUCTION (Page No. — 27)
 1.1 STUDY OBJECTIVES
 1.2 MARKET DEFINITION
 1.2.1 INCLUSIONS AND EXCLUSIONS
 1.3 STUDY SCOPE
 1.3.1 MARKETS COVERED
 FIGURE 1 NEUROMORPHIC COMPUTING MARKET
 1.3.2 GEOGRAPHIC SCOPE
 1.3.3 YEARS CONSIDERED
 1.4 CURRENCY
 1.5 VOLUME UNIT
 1.6 LIMITATIONS
 1.7 STAKEHOLDERS
 1.8 SUMMARY OF CHANGES

2 RESEARCH METHODOLOGY (Page No. — 32)
 2.1 RESEARCH DATA
 FIGURE 2 MARKET: RESEARCH DESIGN
 2.1.1 SECONDARY DATA
 2.1.1.1 Major secondary sources
 2.1.1.2 Key data from secondary sources
 2.1.2 PRIMARY DATA
 2.1.2.1 Primary interviews with experts
 2.1.2.2 Key data from primary sources
 2.1.2.3 Key industry insights
 2.1.2.4 Breakdown of primaries
 2.1.3 SECONDARY AND PRIMARY RESEARCH
 2.2 MARKET SIZE ESTIMATION
 FIGURE 3 RESEARCH FLOW OF MARKET SIZE ESTIMATION
 2.2.1 BOTTOM-UP APPROACH
 FIGURE 4 MARKET SIZE ESTIMATION METHODOLOGY (SUPPLY SIDE): REVENUE FROM SALES OF NEUROMORPHIC COMPUTING PRODUCTS AND SOLUTIONS
 FIGURE 5 MARKET SIZE ESTIMATION METHODOLOGY: BOTTOM-UP APPROACH
 2.2.2 TOP-DOWN APPROACH
 FIGURE 6 MARKET SIZE ESTIMATION METHODOLOGY: TOP-DOWN APPROACH
 2.3 MARKET BREAKDOWN AND DATA TRIANGULATION
 FIGURE 7 DATA TRIANGULATION
 2.4 RESEARCH ASSUMPTIONS
 2.5 RISK ASSESSMENT
 TABLE 1 RISK FACTOR ANALYSIS
 2.6 FORECASTING ASSUMPTIONS
 2.7 COMPETITIVE LEADERSHIP MAPPING METHODOLOGY
 TABLE 2 EVALUATION CRITERIA
 2.7.1 VENDOR INCLUSION CRITERIA
 2.8 LIMITATIONS OF THE STUDY

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