Neuromorphic Computing Market Research Analysis 2024-2033: Growth, Size, Demand, Analysis, Insights

The Neuromorphic Computing Global Market Report 2024 by The Business Research Company provides market overview across 60+ geographies in the seven regions – Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa, encompassing 27 major global industries. The report presents a comprehensive analysis over a ten-year historic period (2010-2021) and extends its insights into a ten-year forecast period (2023-2033).

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According to The Business Research Company’s Neuromorphic Computing Global Market Report 2024, The neuromorphic computing market size has grown exponentially in recent years. It will grow from $1.15 billion in 2023 to $1.44 billion in 2024 at a compound annual growth rate (CAGR) of 25.8%. The growth in the historic period can be attributed to advancements in artificial intelligence (AI), the rise of cognitive computing applications, collaborations between the neuromorphic computing industry, real-time processing requirements, increased investment and funding.

The neuromorphic computing market size is expected to see exponential growth in the next few years. It will grow to $3.4 billion in 2028 at a compound annual growth rate (CAGR) of 23.9%. The growth in the forecast period can be attributed to rise of edge computing, applications in autonomous vehicles, ongoing innovations in neuromorphic hardware, continued research initiatives and academic collaborations, growing demand for brain-inspired computing. Major trends in the forecast period include technological innovation and advancement, advancements in quantum computing technology, advancements in semiconductor technology, competition from established technologies, automated machine learning.

The rising demand for automated systems in industries is expected to propel the growth of the neuromorphic computing market going forward. Automated systems refer to the use of various technologies and control systems to operate and monitor industrial processes without human intervention. Neuromorphic computing technology can be used in autonomous systems such as robotics, drones, self-driving cars, and artificial intelligence. Neuromorphic computing technology can be used to improve automation and efficiency in large-scale operations, product customization, and factories. For instance, in December 2023, according to Eurostat, a Luxembourg-based statistical office of the European Union, around 28% of large EU enterprises used artificial intelligence technologies, and 8% of EU enterprises had implemented artificial intelligence technologies in 2021. Additionally, around 53% of EU enterprises implemented ready-to-use commercial artificial intelligence software or systems in 2021. Therefore, rising demand for automated systems in industries is driving the growth of the neuromorphic computing market.

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The neuromorphic computing market covered in this report is segmented –

1) By Component: Hardware, Software, Services

2) By Deployment: Edge Computing, Cloud Computing

3) By Application: Signal Processing, Image Processing, Data Processing, Object Detection, Other Applications

4) By End Use: Consumer Electronics, Automotive, Healthcare, Military And Defense, Other End Uses

Major companies operating in the neuromorphic computing market are focusing on innovative technology, such as neuromorphic processors, to mimic the human brain. Neuromorphic processors are designed to mimic the structure and function of the human brain and nervous system. For instance, in January 2022, BrainChip, an Australia-based artificial intelligence company, launched Akida, a neural networking processor designed to provide low-power artificial intelligence for edge devices. The Akida Neuromorphic ML Framework is a complete machine learning framework that enables the seamless creation of neuromorphic applications. The Akida Neural Processor SoC (System on Chip) is a fully digital, event-based AI processor that employs a Spiking Neural Network (SNN) model, a type of artificial neural network that mimics how neurons communicate in the brain. SNNs use spikes or pulses of information to represent and transmit data, contributing to the efficiency of event-based processing.

The neuromorphic computing market report table of contents includes: