Delray Beach, FL, Dec. 02, 2024 (GLOBE NEWSWIRE) -- The neuromorphic computing market size is expected to reach USD 1,325.2 million by 2030 growing at a compound Annual Growth Rate (CAGR) of 89.7%, from USD 28.5 million in 2024.
The neuromorphic computing market is poised for rapid growth, driven by the increasing demand for AI-based applications that mimic the brain's neural architecture. The market is shaped by emerging trends, including the shift toward edge computing architectures, rising demand for brain-computer interfaces, and the convergence of quantum computing with neuromorphic systems. The neuromorphic processor segment held the largest market share in the year 2023. The high market share is attributed to its ability to emulate neural network and provide rapid data processing, networks and provide rapid data processing while reducing power consumption compared to traditional processors.
Top Neuromorphic Computing Companies
- Intel Corporation (US)
- IBM (US)
- Qualcomm Technologies, Inc. (US)
- Samsung Electronics Co., Ltd. (South Korea)
- Sony Corporation (Japan)
Neuromorphic Computing Gains Momentum with Rising Demand in Healthcare, Automotive, and Edge Computing Applications
The globalization of neuromorphic computing would further gain its momentum based on the growing demand for high-performance ICs, neuromorphic hardware adoption by the healthcare and automotive industries, and the penetration of edge computing. Neuromorphic chips enhance the performance of diagnostic imaging systems and portable healthcare devices through rapid and efficient data analysis. This implies quicker diagnosis and better patient outcomes. In the case of the automobile industries, neuromorphic processors enhances ADAS capabilities as well as the operation of autonomous vehicles by effectively processing real-time sensory inputs to yield safer, more reliable operation.
Spiking Neural Networks: Revolutionizing Neuromorphic Computing with Energy-Efficient, Brain-Inspired Design
One of the key innovations in neuromorphic computing, Spiking Neural Networks (SNNs) draw their inspiration from the biological nervous system, which can achieve remarkable efficiency and functionality. Unlike the Artificial Neural Networks (ANN) which rely on continuous-valued signals and is, therefore, computationally expensive, normal SNN works through discrete spikes of activity. This spike-based communication is very close to the neuronal activity existing in the biological brains and thus enables SNNs to process information in an event-driven, asynchronous manner. This approach saves an immense amount of power and increases the computational efficiency, thereby making SNNs suitable for real-time data processing with low-energy consumption applications, such as edge computing and IoT devices.
SNN Core Architecture: Unlocking Advanced Temporal Data Processing and Adaptive Learning for Dynamic Applications
SNN core architecture, basically, forms a composition of interconnected neurons that are divided into layers, such as input, hidden, and output, where the neurons send communications with each other via spikes that represent the timing and intensity of signals. This communication through spikes enables SNN to be much more natural in processing temporal data, such as speech recognition, sensor data processing, and even complex recognition of any pattern in dynamic environments. SNNs also support online learning paradigms whereby the network adapts and learns progressively with new data, just as the biological brain has plasticity. It is fundamental for applications involving adaptive behavior as well as learning under changing conditions-in other words, autonomous vehicle or robotics.
Consumer Electronics Segment Leads Neuromorphic Computing Market with Growing Adoption in Drones and Embedded Systems
Consumer electronics segment is expected to hold high market share during the forecast timeframe. The high market share is credited to growing adoption of neuromorphic chips in consumer drones and embedded systems. Neuromorphic computing plays a transformative role in consumer drones by enhancing their autonomous capabilities and real-time processing efficiency. The use of neuromorphic chips gives the ability to process the sensory data-like images and other environmental inputs-quicker and with less power compared to traditional computing methods. Such possibilities have enabled advanced functionalities that facilitate obstacle avoidance and precise navigation and adaptive flight control for better reliability and efficiency in applications like aerial photography, mapping, and surveillance for drones.
North America Dominates Neuromorphic Computing Market, Driven by AI Investments and Supportive Policies Like Canada’s AIDA
The North American market represents a large share of neuromorphic computing. Increasing government investment in AI technology expansion, significant advancements in research and development in neuromorphic computing and development of new technologies is driving the growth of the neuromorphic computing market in the region. The Canadian government's focus on advancing artificial intelligence technology is expected to significantly boost the growth of neuromorphic computing in the coming years.
A prime example is the introduction of the Artificial Intelligence and Data Act (AIDA) in June 2023. This legislation aims to mitigate the potential risks associated with AI, foster trust in Canada's AI sector, and safeguard Canadians from various harms. By establishing stringent regulations and promoting ethical AI practices, AIDA positions Canada as a leader in responsible and trustworthy AI innovation. This supportive regulatory environment is likely to encourage further development and adoption of neuromorphic computing technologies, which are integral to the advancement of AI.
