The Neuromorphic Computing Market is expected to be valued at USD XX thousand in 2020 and is expected to reach USD 550,593 thousand by 2027, at a CAGR of 89.1% between 2021 and 2027.
Latest added Neuromorphic Computing Market research study by MarketDigits offers detailed product outlook and elaborates market review till 2027. The market Study is segmented by key regions that is accelerating the marketization. At present, the market is sharping its presence and some of the key players in the study are Samsung Electronics, Intel Corporation, HRL Laboratories, Numenta, CEA-Leti, IBM. The study is a perfect mix of qualitative and quantitative Market data collected and validated majorly through primary data and secondary sources.
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This report studies the Neuromorphic Computing Market size, industry status and forecast, competition landscape and growth opportunity. This research report categorizes the Neuromorphic Computing Market by companies, region, type and end-use industry.
Scroll down 100s of data Tables, charts and graphs spread through Pages and in-depth Table of Content on “Neuromorphic Computing Market, By Technology (Hardware, Software), Industry Vertical (IT-Telecom, Medical, Consumer Electronics, Aerospace- Defense, Automotive, Industrial, Others), Application (Data Mining, Image Recognition, Object Detection, Signal Recognition) and Geography – Global Forecast to 2027”. Early buyers will get 10% customization on study.
To Avail deep insights of Neuromorphic Computing Market Size, competition landscape is provided i.e. Revenue Analysis (M $US) by Company (2018-2020), Segment Revenue Market Share (%) by Players (2018-2020) and further a qualitative analysis is made towards market concentration rate, product/service differences, new entrants and the technological trends in future.
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This report would help you answer the following questions:
- What is the market size and forecast of the Global Neuromorphic Computing Market?
- What are the inhibiting factors and impact of COVID-19 shaping the Global Neuromorphic Computing Market during the forecast period?
- Which region outstands in the Global Neuromorphic Computing Market?
- Which are the segments to invest in over the forecast period in the Global Neuromorphic Computing Market?
- What is the competitive strategic window for opportunities in the Global Neuromorphic Computing Market?
- What are the technology trends and regulatory frameworks in the Global Neuromorphic Computing Market?
- What are the major company in the Global Neuromorphic Computing Market?
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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, several factors, such as lack of knowledge about neuromorphic computing and complex algorithms increasing complexity of designing hardware of neuromorphic chips are hindering the growth of the neuromorphic computing market. Moreover, matching a human’s flexibility and ability to learn from unstructured stimuli data can act as a key challenge in the market during the forecast period.
Impact of Covid-19 on neuromorphic computing market:
The COVID-19 pandemic has impacted the growth of the neuromorphic computing market in several verticals worldwide. Neuromorphic chips started being used in medical devices in 2020. Watson, an analytical tool developed by IBM Corp. (US), which works like a human analytical system, is planned to be integrated with neuromorphic chips and is expected to be used for medical imaging analytics. This is expected to drive the growth of the market for the medical industry. With work from home (WFH) becoming the new trend due to the pandemic, there is an increase in the supply of and demand for IT peripherals, which is fueling the growth of the neuromorphic computing market in the IT & telecommunication vertical.
Drivers: Need for better-performing ICs
A central processing unit (CPU) has to constantly shuttle information back and forth from the memory store since it stores data and program instructions in a block of memory, which is separate from the processor that carries out the instructions. Thus, data transportation causes excessive power consumption and slows down the optimum speed of the processor operation, thereby limiting its overall processing speed. While. neuromorphic chips can process data in parallel, and information can be stored in the chip itself. Integration of processing and storage avoids data shuttling, and hence, computing can be more efficient. The need for better performing ICs will be a significant driver to the neuromorphic computing market during the forecast period.
Increase in demand for artificial intelligence and machine learning
The rising number of machine-to-machine connections and the growing penetration of artificial intelligence are driving the need for having more solutions at the edge using neuromorphic computing. Artificial intelligence (AI) has applications 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 drive business environments with smart decisions. Such systems would have applications in image classification, question-answering medical diagnostics systems, fraud detection, credit scoring, speech recognition, language translation, and self-driving automobiles.
Restraints: Complex algorithms increase the complexity of designing hardware of neuromorphic chips
Neuromorphic chips are expected to have a huge growth potential in markets such as mobile and embedded systems. However, at this stage, it will be critical to attaining performance equivalent to graphics processing units (GPUs) in industrial machine learning, as backend server/data center applications need extensive learning of algorithms. Therefore, due to the limitations of hardware fabrication capabilities, it would be difficult to enable neuromorphic hardware to learn complex algorithms for large-scale implementations. This can occur as a hindering factor in the growth of the neuromorphic computing market.
Software by offering segment is projected to witness the highest CAGR for the forecast period
The software segment is expected to grow at a higher between 2021 and 2027. The neuromorphic computing software has applications such as continuous online learning, real-time data streaming, prediction, and data modeling. Increasing adoption of software in industries such as aerospace & defense, IT & telecom, and medical is also driving the growth of the market for neuromorphic computing software.
Aerospace, Military & Defense to account for largest share of neuromorphic computing market during the forecast period
The neuromorphic computing market has been segmented on the basis of vertical into aerospace, military & defense, medical, IT & telecommunication, automotive, industrial, and others. Aerospace, military & defense is expected to hold the largest share of ~32% of market in 2021. Neuromorphic computing can process information faster than any other processor; this helps the military & defense industry in processing sensitive data such as battlefield data, including weapons and resources management. Increasing the requirement for testing and transmitting signals to securely code and transmit data from one end to another to reduce the threat of security issues is another factor driving the growth of the market for the aerospace, military & defense industry.
APAC is attributed to grow at the highest CAGR in the neuromorphic computing market during the forecast period (2021-2027)
North America is expected to hold the largest share of ~40% of the global neuromorphic computing market in 2021. Extensive awareness about the benefits of neuromorphic computing in industries such as aerospace, military & defense and medical is a major driver for the dominance of this region. The US, being the leading adopter of artificial intelligence for machine learning, natural language processing (NLP), image processing, and speech recognition across industries such as medical and automotive, is driving the growth of the market in North America. While APAC is expected to hold the second-largest share of ~37% and is expected to grow at the highest CAGR during 2021-2027. Countries such as China, Japan, and South Korea are expected to be the major contributors to market in APAC. China is the largest market for AI, followed by Japan, in APAC; this makes the country an attractive market for neuromorphic computing for machine learning and NLP applications.
Key Market Players
The neuromorphic computing market was dominated by, Intel Corporation (US), IBM Corporation (US), BrainChip Holdings Ltd. (US), Qualcomm Technologies, Inc. (US) and HP Enterprise (US).
Intel Corporation (US)
Intel Corporation ranked first in the neuromorphic computing market in 2020. Intel, in collaboration with Purdue University, started to work on the design of the neuromorphic chip. In 2012, Intel revealed its spin-based neuromorphic microchips as the ultimate parallel processor and was planning to progress on system-level modeling 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 the 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.
Scope of the Report:
This report categorizes the neuromorphic computing market based on offering, deployment, application, vertical and region
Neuromorphic Computing Market, By Offering
Neuromorphic Computing Market, By Deployment
- Edge Computing
- Cloud Computing
Neuromorphic Computing Market, By Application,
- Image Recognition
- Signal Recognition
- Data Mining
Neuromorphic Computing Market, By Vertical
- Aerospace, Military, & Defense
- Consumer Electronics
- IT & Telecommunication
- Others (Smart Infrastructure & Education)
- North America
- Rest of Europe (RoE)
- Asia Pacific (APAC)
- South Korea
- Rest of APAC (RoAPAC)
- Rest of the World (RoW)
- South America
- Middle East & Africa
- In March 2020, Intel announced the readiness of Pohoiki Springs, its latest and most powerful neuromorphic research system providing a computational capacity of 100 million neurons. The cloud-based system is made available to members of the Intel Neuromorphic Research Community (INRC), extending their neuromorphic work to solve larger, more complex problems.
- In June 2019, Intel 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 working mechanism of 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 such as sparse coding, graph search, and constraint satisfaction problems.
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