by Neuromorphic chips, also called brain-like or neural processing units, are analog or digital electronic circuits that are modeled on the neural structure and/or operation of the brain. They are designed to emulate neuro-biological architectures present in the nervous system and brain by mimicking neural interconnected structures using a variety of tools ranging from digital to analog mixed-signal design. Neuromorphic chips provide advantages such as low power consumption, compact size, ability to process massive amounts of data, and function in real-time making them suitable for applications involving sensing, pattern recognition, control, and cognitive computing. The need for more power-efficient AI solutions for edge and embedded applications is fueling investment and interest in neuromorphic computing technology which provides an alternative to conventional von Neumann based architectures.
The global Neuromorphic Chip Market is estimated to be valued at US$ 44.8 Bn in 2024 and is expected to exhibit a CAGR of 22% over the forecast period 2024 to 2030, as highlighted in a new report published by Coherent Market Insights.
Market key trends:
One of the key trends driving growth in the Global Neuromorphic Chip Market Size is the rising demand for AI edge computing solutions. With the proliferation of IoT devices generating huge volumes of data at the edge, there is a need for low-power AI chips that can process this data locally without needing to send it to the cloud. Neuromorphic chips are well-suited for edge AI applications because their brain-inspired design allows highly parallelized and low-power processing of sensory data. This is expected to boost their adoption in edge devices for applications like industrial IoT, smart cities, autonomous vehicles etc. over the forecast period.
Porter’s Analysis
Threat of new entrants: The threat of new entrants is moderate as manufacturing new chips requires significant capital investment and R&D. However, technology advancements are lowering the entry barriers.
Bargaining power of buyers: The bargaining power of buyers is low to moderate due to the presence of many chip manufacturers offering substitutable products. However, customers can negotiate on price or demand bundled services.
Bargaining power of suppliers: The bargaining power of suppliers is moderate as key materials like semiconductors have few substitutes and manufacturers depend on a limited number of semiconductor suppliers.
Threat of new substitutes: The threat of new substitutes is high as new technological advancements in artificial intelligence pose a potential threat.
Competitive rivalry: The competitive rivalry is high due to the presence of many established players offering similar products. Players differentiate through technical expertise and competitive pricing.
Key Takeaways
The global neuromorphic chip market is expected to witness high growth over the forecast period. The global Neuromorphic Chip Market is estimated to be valued at US$ 44.8 Bn in 2024 and is expected to exhibit a CAGR of 22% over the forecast period 2024 to 2030.
The North American region dominates the market currently due to significant investments in R&D of neuromorphic chips by US companies. The Asia Pacific region is expected to grow at the fastest pace during the forecast period. This can be attributed to increasing investments by countries like China and Japan to develop indigenous neuromorphic technology. Countries in this region offer manufacturing facilities at lower costs.
Key players: Key players operating in the neuromorphic chip market are General Vision, Intel Corporation, IBM Corporation, Qualcomm, and Samsung Electronics. General Vision offers neuro-inspired TrueNorth chips, while Intel focuses on Loihi, neuromorphic research chip. IBM is developing synthetic brain projects, whereas Qualcomm invests in neuromorphic sensor technology. Samsung offers memory and semiconductor chips for neuromorphic applications.
*Note:
1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
