Future of Neuromorphic Computing Market 2024 to 2030
As we navigate an era of rapid technological advancement, neuromorphic computing is emerging as a transformative force in artificial intelligence and cognitive computing. Designed to mimic the brain's architecture, these systems offer groundbreaking potential for efficiency and processing power. Here are some key trends and predictions shaping the neuromorphic computing market:
The neuromorphic computing market is expected to grow from USD 28.5 million in 2024 and is estimated to reach USD 1,325.2 million by 2030; it is expected to grow at a Compound Annual Growth Rate (CAGR) of 89.7% from 2024 to 2030.
Growth in the neuromorphic computing market is driven through the integration of neuromorphic computing in automotive and space operations. In space, where bandwidth is limited and the communication delay might be considered large, onboard processing capabilities are crucial. The neuromorphic processor analyzes and filters data at the point of collection, reducing the need to transmit large datasets back to Earth. whereas, in automobile sector, neuromorphic processors can make autonomous driving systems more resresponsive by onboard real-time processing with minimal latency so that safety is ensured along with efficiency.
Neuromorphic Computing Market Trends & Dynamics
DRIVER: Growing application of AI and ML fueling demand for neuromorphic computing
Neuromorphic computing can handle massive data streams and parallel processing more effectively than conventional ICs. This is crucial as AI models become more complex and data-intensive, necessitating chips that can provide high performance with low energy consumption. As industries seek more powerful and efficient computational solutions, such flexibility and efficiency in the neuromorphic computing domain make it one of the most valuable tools in the current AI revolution.
As demand for Al and ML capabilities increases in the healthcare and automobile industries, the market for neuromorphic computing is poised for high growth. In May 2024, SpiNNcloud Systems (Germany) launched SpiNNaker2, the latest neuromorphic computing platform that has been designed to support hybrid AI systems with increased computing speed and reduced power consumption. In April 2024, Intel (US) announced the creation of the biggest neuromorphic system comprising 1.15 billion neurons and using Intel's Loihi 2 processor, called Hala Point. It has been installed at Sandia National Laboratories to promote research into brain-inspired AI and the efficiency and sustainability of AI technologies. These developments represent significant strides toward advancing the functionality of neuromorphic computing in response to growing needs for AI and ML.
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OPPORTUNITY: Increasing adoption of neuromorphic computing solutions in healthcare sector
There is a growing potential of AI and neuromorphic computing in medical science and medical imaging. Neuromorphic technologies revolutionize healthcare, by streamlining workflow in the medical services, increase accuracy in diagnosis, and enhancing the outcome of treatment. The incorporation of neuromorphic chips with medical imaging devices leads to faster processing and quicker diagnosis with greater productivity in facilities. Artificial intelligence in neuromorphic hardware could handle large datasets, help radiologists prioritize cases, and come up with new applications in multi-modal imaging that will support early detection of diseases or personalized treatments.
The recent demands for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry have led healthcare professionals to seek new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which implements neural models in hardware and software to mimic certain brain-like behaviors, help usher in a new era of medicine through low-power, low-latency small-footprint and high-bandwidth solutions. The major health technology companies, for instance, IBM, Philips, GE, and Siemens, continue to invest heavily in these solutions, which fuels innovation and speeds up adoption in the market globally.
Natural language processing (NLP) segment is projected to grow at a high CAGR of neuromorphic computing market during the forecast period.
Natural Language Processing (NLP) is a branch of artificial intelligence focused on giving computers the ability to understand text and spoken words in much the same way human beings can. NLP represents a promising application of neuromorphic computing, leveraging the brain- inspired design of spiking neural networks (SNNs) to enhance the efficiency and accuracy of language data processing. Low-power, high-performance solutions are required by the expanding demand for real-time efficient language processing in devices-from smartphones to IoT devices.
Neuromorphic computing fits well within these requirements with its energy-efficient architecture. Progress over time with improvements in SNNs is also advancing its ability to approach complex NLP tasks, which are closer to being adapted for commercial and industrial markets. SNNs provide improved energy efficiency, demonstrated through being able to achieve up to 32x better energy efficiency during inference and 60x during training compared with traditional deep neural networks, further underlines the benefits of adding neuromorphic computing to NLP systems. Besides cost-efficiency in the field of NLP systems, such efficiency enables deploying complex language models even on devices with reduced resources. This leads to making neuromorphic NLP applications even more relevant to wider adoption and growth.
Industrial vertical in neuromorphic computing market will account for the high CAGR by 2030.
Industrial segment will account for the high CAGR in the forecasted period. In the industrial vertical, manufacturing companies use neuromorphic computing for developing and testing end products, manufacturing delicate electronic components, printing products, metal product finishing, testing of machines, and security purpose. Neuromorphic computing can be used in these processes to store the data in chips, and the images can be extracted from the devices for further use. Neuromorphic computing also helps monitor the condition of the machines by analyzing the previous signals and comparing them with current signals. These advantages lead to high demand for neuromorphic processors and software in industrial vertical.
Asia Pacific will account for the highest CAGR during the forecast period.
The neuromorphic computing market in Asia Pacific is expected to grow at the highest CAGR due to a high adoption rate of new technologies in this region. High economic growth, witnessed by the major countries such as China and India, is also expected to drive the growth of the neuromorphic computing market in APAC. BrainChip, Inc. (Australia), SynSense (China), MediaTek Inc. (Taiwan), SAMSUNG (South Korea), Sony Corporation (Japan), are some of the key players providing neuromorphic hardware and software in the region. In China, Japan, South Korea, and Singapore, for instance, significant investments have been made in neuromorphic research and infrastructure. This has fostered close relationships between academia, industry, and government, facilitating major breakthroughs in machine learning, natural language processing, and robotics that have propelled the development of neuromorphic technologies.
Key companies operating in the neuromorphic computing market are