Neuromorphic Chip Market Size, Share & Trends Analysis Report By Application (Image Recognition, Signal Recognition, Data Mining, Process Optimization, Others), By Offerings (Hardware, Software), By End-user Industry (Financial Services and Cybersecurity, Automotive (ADAS/Autonomous Vehicles), Industrial (IoT Ecosystem, Surveillance, and Robotics), Consumer Electronics, Other End-user Industries (Medical, Space, Defense, etc.)) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2024-2032

Neuromorphic chip market size

The global neuromorphic chip market size was valued at USD 65.43 million in 2023. It is expected to reach USD 2,175.47 million in 2032, growing at a CAGR of 47.6% over the forecast period (2024-32). Neuromorphic chips mimic the structure and functionality of the human brain, enabling efficient processing of complex AI algorithms and neural networks. As AI applications expand across industries such as healthcare, automotive, robotics, and finance, the demand for neuromorphic chips increases.

Current deep-learning techniques and associated hardware face several hurdles, such as the economics of Moore's Law, which makes it significantly difficult for a start-up to compete in the AI space, limiting competition. The data overflow makes present memory technologies a limiting factor. Also, the exponential increase in computing power needs has created a heat wall for each application. Meanwhile, the market demands more real-time speech recognition and translation performance, real-time video understanding, and real-time perception for robots and cars. Several applications require more intelligence that combines sensing and computing.

These substantial hurdles created a disruption that led to a new technology paradigm in which start-ups can differentiate themselves. This could utilize the benefits derived from emerging memory technologies and significantly improve bandwidth, data, and power efficiencies. The latest paradigm is the neuromorphic approach, an event-based approach where computation happens only if required instead of happening at each clock step. The method allows tremendous energy-saving essential to run power-intensive AI algorithms. This is driving the usage of neuromorphic chips as it is the probable next step in AI technology.

Growth Factors

Increasing demand for artificial intelligence-based microchips

Significant corporate investment is being made in artificial intelligence (AI), and the chip market is drawing more and more interest from the markets. End users currently adopt many applications, and many new applications are anticipated to appear soon. CPUs and AI accelerators are currently accessible semiconductors for AI applications. Because CPUs are limited in their ability to do computations, AI accelerators are dominating the market. Application-Specific Integrated Circuits (ASICs), GPUs, and Field-Programmable Gate Arrays are all AI accelerators now on the market (FPGAs). GPUs have a significant advantage when processing AI training and inference since they contain many parallel processing cores. They do, however, consume a lot of power, which makes them unsustainable for use in the future.

However, despite having lesser performance, new FPGAs can be ten times more power-efficient than GPUs. FPGAs can substitute in applications where energy efficiency is the top priority. ASICs exhibit the best performance, lowest power consumption, and efficiency among AI Accelerators. Research and development on AI primarily focus on improving and utilizing deep neural networks and AI accelerators. AI depends upon generating near-real-time data analysis. Neuromorphic computing is intended to cover this gap by emulating certain aspects of brain functions. This brain-inspired architecture, combining computation and memory simulating neurons and synapses, can potentially achieve the requirements of next-generation AI systems.

Emerging trend of combining the concept of neuroplasticity with electronics

Current computers are considerably limited by the amount of power needed to process large volumes of data. However, biological neural systems process substantial volumes of information in complex ways while consuming significantly less power. Power savings are witnessed in neural systems by the sparse utilization of hardware resources in time and space. Since several real-world problems are power-limited and should process extensive volumes of data, neuromorphic chips offer a significant promise. The human brain's structure changes throughout life as one learns and takes on new tasks; a phenomenon called neuroplasticity. Engineers of neuromorphic chips are integrating the concept of neuroplasticity into electronics.

• For instance, in Intel's neuromorphic chip Loihi, the chip comprises a many-core mesh of neurons capable of supporting recurrent and hierarchical neural network topologies.

In March 2020, Intel announced the readiness of Pohoiki Springs, its most potent and latest neuromorphic research system offering the computational capacity of almost 100 million neurons. The cloud-based system is available to the Intel Neuromorphic Research Community (INRC) members, extending their neuromorphic work to solve more significant, complex problems. The system comprises 24 Nahuku boards with 32 chips each, integrating a total of 768 Loihi chips. Multiple programs, such as Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE), are emerging, supporting a multidisciplinary approach to coordinating significant technology development activities in architecture, hardware, and simulation. The first phase of SyNAPSE developed nanometer-scale electronic synaptic components that could vary connection strength between two neurons analogous to that seen in biological systems and simulated the utility of these synaptic components in core microcircuits supported the overall system architecture.

Restraining Factors

Need for a high level of precision and complexity in hardware design

The design of neuromorphic chips follows the goal of modeling parts of the biological nervous system. The aim is to reproduce its computational functionality and especially its ability to efficiently solve cognitive and perceptual tasks. Achieving this requires modeling networks of sufficient complexity in terms of the number of neurons and the number of synaptic connections. The brain and its ability to learn and adapt to specific problems are still subject to basic neuroscientific research. Highly integrated analog circuit arrays, complex interfaces, and the difficulties and pitfalls of physical standard-cell design could push standard tooling to its limits. This might be a common denominator of most neuromorphic hardware designs. Therefore, developing non-standard design flows or custom tools is integral to the overall design process.

Furthermore, Analog circuits are prone to multiple parameter deviations due to mismatch effects and need additional calibration to reach a target operating point. While individual components could often be unit-tested with traditional simulation strategies, the assessing ability of a complete circuit's functionality is limited due to inter-dependencies and error propagation of parameters. Especially for complex circuits coupled with high-dimensional parameter spaces, multidimensional dependencies might be hard to resolve. Verifying such complex circuits is hence a significant challenge.

Market Opportunities

Development of new techniques

New techniques are being tested for making neuromorphic chips at the university level.

• For instance, in April 2021, FRANZ, a global ceramic brand, partnered with the Department of Biomedical Engineering of National Yang-Ming Chiao Tung University to adopt a new technique to produce neuromorphic chips through ceramic 3D printing technology. Such chips aim to detect neuroelectric signals and neurotransmitter concentration and provide deep brain stimulation. In the future, these neuromorphic chips may be applied in medical care for treating diseases, such as neurodegenerative disorders. Hence, the development of such techniques provides immense potential for market growth.

Regional Insights

North america is a dominant region with 46.7% market share

North America accounted for the largest market share and is estimated to grow at a CAGR of 46.7% during the forecast period. Some of the most significant market players, including Intel Corporation and IBM Corporation, are based in North America. Due to factors including government initiatives, investor activity, and other reasons, the market for neuromorphic chips is expanding in the region. One of the significant factors behind the growth of the market in North America is the interest shown by government bodies in neuromorphic computing.

• For instance, the Department of Energy (DOE) announced in September 2020 financing of USD 2 million for five basic research programs to develop neuromorphic computing. The DOE effort encourages the creation of hardware and software for neuromorphic computing inspired by the human brain.

On the other hand, the Canadian government is concentrating on AI technology, which is also anticipated to open up opportunities for growth in neuromorphic computing over the future years.

• For instance, the governments of Canada and Quebec collaborated in June 2020 to encourage the ethical advancement of AI. The emphasis will be on many topics, including dependable AI, commercialization, data governance, and future work and innovation. Increased AI-based processors drive the market for neuromorphic chips in Canada.

Europe is the second largest region. It is estimated an expected value of USD 360 million by 2030, registering a CAGR of 48.9%. A rise in neuromorphic chips is also anticipated in the European region due to governmental initiatives, vendor investments, etc. Collaborations are drawn to several lengthy research projects aiming to enhance neuromorphic technology.

• For instance, in April 2021, CEA-Leti, a research institute for electronics and information technologies based in France, announced the launch of EU projects to develop a novel class of algorithms, devices, and circuits that reproduce multi-timescale processing of biological neural systems. The results are expected to build neuromorphic computing systems that can efficiently process real-world sensory signals and natural time-series data in real-time and demonstrate this with a practical laboratory prototype. The project brings together European organizations, such as Imec, IBM Switzerland, the University of Zurich, CSIS, CNR, SynSense, and UOG. The project is expected to finish by June 2023, and the European Union has contributed over EUR 3 million. The Human Brain Project (HBP) in Europe is a ten-year project launched in 2013. The project is in its final phase (April 2020 to March 2023), focusing on three core areas: brain networks, their role in consciousness, and artificial neural networks. Recently, the project facility hubs became operational for fostering collaboration and scientific research. Local vendors in the area are concentrating on creating neuromorphic semiconductors thanks to investment from numerous market venture capitalists. Such expenditures are anticipated to influence the market's innovation.

Asia pacific is the fastest growing region

Asia-Pacific is the third largest region. The Asia-Pacific region is one of the fastest adopters of technology. The region is rapidly growing in neuromorphic technology due to government support, research investments, and innovation activities. In March 2021, the Chinese government announced that the country would increase research and development spending by more than 7% between 2021 and 2025 to pursue technological breakthroughs. In its 14th five-year plan, the country laid out seven technology areas to focus research on, including artificial intelligence, quantum computing, semiconductors, and space. The technology focuses on brain science, also called brain-computer fusion technology, which could help treat diseases. As part of its broad strategy to become a global leader in AI theories, technologies, and applications by 2030, China indicated that its ability to produce cutting-edge AI chips indigenously would be integral to its success. To overcome challenges in the production of chips and self-reliance, vendors in the country are stepping into developing AI chips.

• For instance, ByteDance is also making plans to build semiconductors. The company has a team to explore the development of AI chips. Such activities are creating growth opportunities for neuromorphic chips in the country.

Segmentation Analysis

By end-user industry

By end-user industry, the global neuromorphic chip market is segmented into financial services and cybersecurity, automotive (ADAS/autonomous vehicles), industrial (IoT ecosystem, surveillance, and robotics), consumer electronics, and other end-user industries (medical, space, defense, etc.). Consumer electronics accounted for the largest market share and is estimated to grow at a CAGR of 45.7% during the forecast period. The consumer electronics industry recognizes neuromorphic computing as a promising tool for enabling high-performance computing and ultra-low power consumption to achieve these goals. For instance, AI services, such as Alexa and Siri, rely on cloud computing with the internet to parse and respond to spoken commands and questions. Neuromorphic chips have the potential to allow several varieties of sensors and devices to perform intelligently without requiring an internet connection. Smartphones are expected to be the trigger for the introduction of neuromorphic computing. Several operations, such as biometrics, are power-hungry and data-intensive. For instance, in speech recognition, audio data is processed in the cloud and then returned to the phone.

Additionally, Artificial Intelligence (AI) requires more computing power. Still, low-energy neuromorphic computing could significantly push applications presently in the cloud to run directly in the smartphone in the future without substantially draining the phone battery. Rather than handing off AI tasks to cloud systems requiring tons of cooling and power, the low energy need of neuromorphic computing means those tasks could potentially be done by hardware, such as smartphones, tablets, drones, and wearables. Neuromorphic computing could lead to a substantial integrated collaborative technology industry, where computing becomes an end-to-end system design problem. All the factors mentioned above fuel the market growth.

The industrial segment is the second largest. Neuromorphic chips could efficiently process image, voice, and signal data involved in various IoT user interfaces and sensors. Also, the chips are scalable to the server level, which may benefit IoT scenarios that require hybrid architectures. Artificial neural networks are being utilized substantially in solutions ranging from robot control and machine learning to image recognition and game playing. Although the results are effective, they build on a very simplified model of biological neurons. Neuroscience has provided much more accurate models, but they are currently significantly complicated to implement in computers. Instead, scientists and industry are developing alternative computer architectures to support more brain-like computation. The utilization of neuromorphic technology also promises to decrease the power consumption needed for robotics, which is a primary goal for neuromorphic technology. All such factors contribute to market growth.

Recent Developments

• September 2022 - On Day 2 of Intel Creativity, Intel demonstrated how its initiatives and investments to promote an open ecosystem drive community innovation across all levels of the software stack, from silicon to systems to apps.
• September 2022 - The 13th Gen Intel® CoreTM processor family was unveiled by Intel Innovation, highlighted by the 13th Gen Intel® CoreTM i9-13900K, the fastest desktop CPU in the world1. Six new unlocked desktop CPUs with up to 24 cores, 32 threads, and blistering clock rates up to 5.8 GHz are included in the new 13th Gen Intel Core family to provide the most acceptable possible gaming, streaming, and recording experiences.