The global neuromorphic computing market size was valued at USD 4,767.86 million in 2022. It is estimated to reach USD 36,312.57 million by 2031, growing at a CAGR of 25.7% during the forecast period (2023–2031).
Neuromorphic computing is an approach to computer engineering in which computer components are modeled after systems in the human brain and nervous system. The phrase describes the creation of both software and hardware components in computers. Neuromorphic engineering is another name for neuromorphic computing. To design bio-inspired computer systems and hardware, neuromorphic engineers draw on various fields, including computer science, biology, mathematics, electronic engineering, and physics.
Neuromorphic designs are most frequently modeled after neurons and synapses among the biological components of the brain. This is so because, according to neuroscientists, neurons are the brain's basic building blocks. Information is communicated between various parts of the brain and the rest of the nervous system via neurons using chemical and electronic impulses. Synapses are the connections between neurons. Unlike conventional computer systems, neurons and synapses are more adaptable, flexible, and energy-efficient information processors.
An integrated circuit (IC) is a semiconductor wafer that contains tiny, fabricated resistors, capacitors, and transistors with a count of thousands or millions. An IC can be analog or digital, depending on its intended application. High computing speed and low power consumption are a few of the highly desired features of an IC, which is largely driving the growth of the neuromorphic computing market.
The analog circuits are used for designing neuron architecture as they tend to mimic a human brain closely but are noisy and imprecise, which makes it difficult for them to correspond to the mathematical model of the neurons. However, the digital circuits approximate the neural operations very reliably. This property of digital circuits makes them an ideal choice for computational neuroscience research requiring discrete-time simulations. In order to achieve human cognition, neuromorphic chips are witnessing considerable research and development as they can easily meet the demand for ICs that offer high computing speed and low power consumption, thereby driving the market.
A neuron architecture overcomes the use of traditional ICs used in von Neumann architecture to improve the operational efficiency resulting due to frequent exchange of data between the CPUs and the memory units. A neuron architecture unites memory and processor, which helps remove the data exchange between the components and allows solving the issues related to big-scale computation such as neural analysis. In addition, memory and processing have become a single entity in a neuromorphic chip. This shift from traditional ICs to neuromorphic chips will help meet the existing problems in clustering, combinatorial optimization, classification, and robotic actuation, among others.
TrueNorth by IBM Corporation (U.S.) is the company's largest chip to date in transistor count. This chip consumes less than 100mW of power and has a power density of 20mW per square cm. TrueNorth contains 1 million digital neurons and 256 million synapses connected by an event-driven routing infrastructure. The chip combines the brain's left and right hemisphere capabilities, creating holistic computing intelligence. Therefore, the factors above will drive market expansion during the forecast period.
Neuromorphic computing will be able to address the existing complex issues, but designing such hardware is comparatively complex. This integrates memory and processor as one. The memory structures include neurons and synapses, where communication takes place using spikes. Spikes allow for the simplest possible temporal message and facilitate algorithms inspired by biological neural systems. The messages are packed with an address and routed over a network using switching fabric, yielding higher total throughput. The spikes are routed on a multiscale network. The challenge here is the distribution of large amounts of memory or synapses among many processors or neurons on a single chip.
One major constraint is the availability of immature back-end memory technologies such as memristor and PCM. They are also unavailable in the safe operating area (SOA) complementary metal-oxide semiconductor (CMOS). On the other hand, the distribution of memory among processors on a single chip also leads to off-chip memory power burn, and the DRAM requires large array sizes to be space efficient. Creating a large-scale simulation capability that can accurately model the neuromorphic hardware is difficult, as developing a scalable spiking architecture is difficult to attain.
The neuromorphic chips have a parallel architecture and are designed to process information in the very same way that a human brain does. This helps achieve the platform that artificial intelligence (AI) seeks to process information using machine-learning software. The demand for AI services across various verticals that require high computational power and high efficiency can be met using neuromorphic chips.
Neuromorphic chips can resolve various machine-learning issues such as classification, clustering, robotics, and combinatorics. For instance, in the banking, finance, and law sector, a huge amount of data is generated, requiring classification and clustering in real-time. This requires high computing power, whereas using the traditional architecture involves a frequent exchange of information between the CPU and memory unit resulting in low efficiency as it consumes too much energy. The problem can be resolved using neuromorphic computing as the parallel architecture eliminates the need to exchange information, resulting in high efficiency even at low computation power. Such factors create opportunities for market growth.
The global neuromorphic computing market is segmented by application and end-user.
Based on application, the global neuromorphic computing market is bifurcated into signal processing, image processing, data processing, object detection, and others.
The image processing segment dominates the global market and is projected to exhibit a CAGR of 28.80% over the forecast period. Image processing refers to analyzing and manipulating a digitized image to improve quality and extract useful information. The neuromorphic chip is used in image processing for visualization, image sharpening and restoration, image retrieval, measurement of patterns, and image recognition. In addition, the image processing segment leads the data collection and labeling market, accounting for the highest share in the global market. It is also projected to witness the highest growth rate over the forecast period. This is due to the increasing use of computer vision in various industries, including the automotive, healthcare, and media and entertainment sectors.
Medical imaging is one of the most important image-processing applications. The rising trend of gathering insights from large volumes of data sets for surveillance and national security is bolstering market growth. These processes also reduce the probability of spamming and phishing, particularly in the government sector. It subsequently augments the application of neuromorphic computing in image processing systems.
Signal processing is a technique that analyses, synthesizes, and modifies signals by processing data such as audio, video, speech, language, image, and multimedia transferred over a medium. It helps in improving signal efficiency and reducing distortions. The signal processing segment is also expected to account for a prominent share and expand with a significant growth rate during the upcoming period. Signal processing has widespread application for the collection and execution of various types of signals, such as audio, video, speech, language, etc., across diverse industrial applications.
For instance, Robert Bosch GmbH is a Germany-based company that uses SoundSee, audio AI technology consisting of advanced audio signal processing algorithms developed with machine learning. In 2019, SoundSee was launched for ISS (International Space Station) using microphones situated on mini robots for recording the sound of equipment and machinery aboard the station.
Based on end-user, the global neuromorphic computing market is segmented by consumer electronics, automotive, healthcare, military and defense, and others.
The consumer electronics segment owns the highest market share and is estimated to exhibit a CAGR of 26.6% during the forecast period. Consumer electronics are electronic devices used by individuals either for personal use or non-commercial/professional purposes. Consumer electronics include televisions, wearable devices, smartphones, and washing machines. These devices require neuro control units and machine-learning chips for automation and processing. In addition, neuromorphic chips are expected to witness high adoption for wearable devices as they possess capabilities such as pattern matching and easy identification of actions and motions for devices using the Intel Curie module. The consumer electronics segment will dominate over half the global revenue throughout the forecast period. This is attributable to the dynamic growth witnessed in the electronics industry and the integration of IoT and AI-based technologies in consumer electronic equipment.
The scope of application for neuromorphic computing has increased in the automotive sector owing to advantages such as increased functionality, improved reliability, automated functionality, and improved thermal capabilities. It is widely used in self-driving vehicles, car infotainment systems, and electronic control units. In addition, the automotive industry is projected to expand with the highest expansion throughout the forecast period owing to the development of automotive processors having applications in connected cars and autonomous vehicles. For instance, In January 2021, Qualcomm Inc. announced its collaboration with Amazon pre-integrate Qualcomm Snapdragon Automotive Cockpit Platforms with the Alexa Custom Assistant. The digital cabin assistance system will have superior, highly-intuitive voice-based proficiencies for conversational interactions with passengers and drivers.
Based on region, the global neuromorphic computing market is bifurcated into North America, Europe, Asia-Pacific, Latin America, and the Middle East and Africa.
North America is the most significant global neuromorphic computing market shareholder and is estimated to exhibit a CAGR of 26.3% during the forecast period. The dominance of this region may be attributed to the wide presence of key market players such as General Vision Inc., IBM Corporation, Intel Corporation, and HRL Laboratories LLC, who are actively involved in developing neuromorphic chips. Organizations such as IBM Corporation and HRL Laboratories LLC have received funding from DARPA for advancements in neuromorphic computing. In addition, early adopters in the North American market, like the U.S. and Canada, are the frontiers of neuromorphic computing system applications.
One of the most important trends in the area is the use of AI for voice and speech recognition. For instance, a U.S.-based AI company, Globalme Localization Inc., delivered the accent and dialect audio collection to Sonos Inc., a U.S.-based audio company. Sonos Inc. unified its wireless speakers with smart home assistants by collecting speech and accent data across three countries. The integration allowed fine-tuning of its speech recognition engines, enhancing the voice experience.
Asia-Pacific is estimated to exhibit a CAGR of 28.6% over the forecast period. Asia-Pacific is predicted to exhibit the highest growth over the forecast period. The growth is attributable to the rapidly increasing consumption of smart electronic equipment, swift technological advancements, and the increasing prominence of social networking in developing economies such as India and China. The increasing number of smart devices boosts data and signal processing system requirements. In addition, the surging applications of face recollection in surveillance and security systems in China are estimated to fuel market growth in the region. For instance, the Chinese government has imposed real-name registering policies across the country, under which inhabitants should mandatorily link their official government ID with an online account. Such policies are augmenting the use of data processing applications across the country.
Europe is also projected to witness notable expansion during the forecast period. The increasing developments in automotive obstacle detection technologies are expected to fuel the market growth in the European region's automobile sector over the forecast period. In addition, the increasing use of biometry in European nations is catering to a whole new implementation area for the image-processing applications of neuromorphic computing. For instance, in January 2020, Germany's Interior Minister announced a plan to use biometric applications at 14 airports and 134 railway stations to expand security applications in Germany.
In the Middle East and Africa, increasing investments in the surveillance and telecommunication industry have led to the need for image and data processing applications. Subsequently, creating high growth virtues for the market growth over the coming years. For instance, several African countries, such as Kenya and Uganda, have received infrastructure and financing from Chinese companies, including Huawei Technologies Co., Ltd., to develop surveillance and telecommunications.
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