Neuromorphic Computing Industry

According to a research report "Neuromorphic Computing Market by Offering (Processor, Sensor, Memory, Software), Deployment (Edge, Cloud), Application (Image & Video Processing, Natural Language Processing (NLP), Sensor Fusion, Reinforcement Learning) - Global Forecast to 2030" The neuromorphic computing industry 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 industry 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 responsive by onboard real-time processing with minimal latency so that safety is ensured along with efficiency.

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By Offering, software segment is projected to grow at a high CAGR of neuromorphic computing industry during the forecast period.

The software segment is expected to grow at a fast rate in the forecasted period. Neuromorphic software has its roots in models of neural systems. Such systems entail spiking neural networks (SNNs), that attempt to replicate the properties of biological neurons in terms of their firing patterns. In contrast to the typical artificial neural networks using continuous activation functions, SNNs utilize discrete spikes for communication, a feature that is also found in the brain. With intelligence embedded directly into the edge devices and IoT sensors, the potential for neuromorphic systems to perform even the most complex tasks such as pattern recognition and adaptive learning with considerably less power consumption remains. This efficiency stretches the lifetime of device operations while cutting down on the overall energy footprint, thus spurring demand for neuromorphic software that can harness these benefits and optimize performance for real-world edge and IoT applications.

By deployment, cloud segment will account for the highest CAGR during the forecast period.

Cloud segment will account for the high CAGR in the forecasted period. Cloud computing benefits from offering central processing power, which enables large-scale computational resources and storage capacities accessible from remote data centers. This is useful because neuromorphic computing, has been very often associated with complex algorithms and large-scale data processing. In the cloud, such huge resources can be utilized to train neuromorphic models, run large-scale simulations, and process enormous datasets. The scalable infrastructure of cloud platforms allows neuromorphic computing applications to dynamically adjust resources according to demand. It is a key factor for the training and deployment of high-scale neuromorphic networks, as their computation requirements are considerable especially during peak loads, driving its demand in the market.

Natural language processing (NLP) segment is projected to grow at a high CAGR of neuromorphic computing industry 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 industry 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 industry 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 industry 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 Players

Key companies operating in the neuromorphic computing industry are Intel Corporation (US), IBM (US), Qualcomm Technologies, Inc. (US), Samsung Electronics Co., Ltd. (South Korea), Sony Corporation (Japan), BrainChip, Inc. (Australia), SynSense (China), MediaTek Inc. (Taiwan), NXP Semiconductors (Netherlands), Advanced Micro Devices, Inc. (US), Hewlett Packard Enterprise Development LP (US), OMNIVISION (US), among others.

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