This should answer your question:
https://www.yolegroup.com/product/report/neuromorphic-computing-memory-and-sensing-2024/
NEUROMORPHIC SENSING AND COMPUTING MARKETS ARE EXPECTED TO GENERATE $8.4B BY 2034
Neuromorphic sensing and computing are poised for significant growth. In 2029, the neuromorphic sensing market could reach $410M and $2.9B by 2034, while the neuromorphic computing market is expected to grow to $412M by 2029 and $5.4B by 2034. For neuromorphic sensing, mobile applications will lead in 2034, followed by entertainment, smart city, automotive, and home applications. For neuromorphic computing, however, data center applications are expected to be the biggest revenue generator in 2034, followed by automotive, entertainment, smart city, and mobile. The embedded emerging NVM market for near- and in-memory computing is expected to remain limited until at least 2029. Near-memory computing approaches – mainly implemented with MRAM – will lead the short-term growth, while analog IMC and other RRAM/PCM-based IMC approaches will ramp up in the following years (>2027).
CONSOLIDATING ECOSYSTEM WITH DIFFERENT STRATEGIES TO HARVEST NEUROMORPHIC REVENUES
The neuromorphic ecosystem has seen increased activity, with notable investments and product demonstrations and announcements since last year (2023). Leading companies, such as IBM, Intel, Sony, and Samsung, are focusing on specific markets, while startups are pursuing more diversified approaches. Traditional processor companies are partnering with neuromorphic firms, enhancing technological differentiation. Some neuromorphic companies target extreme edge AI on battery-powered devices, while others focus on high-performance computing for data centers. In China, giant technology leaders such as Hikvision and Huawei are investing in neuromorphic technologies, mainly for security cameras. In memory technology, most companies are developing digital IC designs utilizing distributed SRAM, with a few adopting SRAM-based or embedded Flash memory cells. Emerging non-volatile memory technologies like PCM and OxRAM show promise but are still in the early stages of development.
LOW LATENCY, POWER EFFICIENCY, AND ONLINE LEARNING ARE THE KEY NEUROMORPHIC ASSETS
The economic feasibility of scaling semiconductor devices is declining despite Moore's Law holding true, as chip development costs rise sharply. Neuromorphic technologies, inspired by biological brains, offer power-efficient solutions for AI tasks, with benefits like low latency and high scalability. They enable real-time edge-AI applications, addressing privacy concerns while utilizing manufacturing nodes from 28nm to 12-7nm for the most advanced. Sensing options include standalone event-based sensors and mixed RGB with event-based pixel hybrids. Neuromorphic computing systems, featuring event-driven processing and spiking neural network algorithms, enable online learning and autonomous robotics. Digital IC designs based on SRAM distributed across logic cores or neurons dominate memory approaches, with analog in-memory computing emerging but facing technical challenges. Overall, neuromorphic technologies show promise for sustainable and efficient AI processing at the edge.”
The problem with this report is that the definition of neuromorphic computing and Edge computing have not one agreed definition.
My opinion only DYOR
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