Neuromorphic computing or neuromorphic engineering is a method of computer engineering in which components of a computer are designed after systems in the human brain and nervous system. The phrase refers to the development of both computer hardware and software.
Goals of Neuromorphic Computing
Neuromorphic computing's two key goals are: First, to create a machine that can learn, recall information, and even draw logical conclusions. This machine is referred to as a cognitive machine. The second goal is to gain a new understanding of how the human brain works, maybe to support a logical theory. The rapidly expanding demand for AI and machine learning across numerous industries, including media and entertainment, aerospace, and the military, as well as the rise in popularity of cognitive and brain robots, are the primary drivers responsible for the expansion of the neuromorphic computing industry.
The domination of North America continues
North America now dominates the neuromorphic computing industry due to the presence of significant neuromorphic chip manufacturers in the area. Europe is expected to experience phenomenal growth between 2022 and 2029 due to the region's expansion and significant investments in neuromorphic projects. During the forecast period, Europe is expected to maintain the second-largest share of the global neuromorphic computing market. The region's neuromorphic computing industry is expanding as more industries, including aerospace and military, IT and telecom, automotive, and others, are adopting IoT-based systems, machine learning, and AI, and the demand for improved process productivity and efficiency rises.
Application base of neuromorphic computing
Based on application, neuromorphic computing's many uses have been categorized as object identification, data mining, signal recognition, image recognition, and other uses. The image recognition market category will dominate the neuromorphic computing industry during the forecast period.
Need for Humanoid Robots
The need for general-purpose humanoid robots with cognitive and cerebral skills is growing, driving the market's expansion. The market's transition from Von Neumann architecture to neuromorphic devices, another market growth driver, is fueled by the latter's built-in technological advantages, including lower power consumption, faster performance, and optimized memory usage. The COVID-19-driven increase in process automation demand throughout the globe has aided in the growth of the neuromorphic computing industry in the IT and healthcare sectors.
Loihi Neuromorphic chip
Scientists have turned to neuromorphic computing to keep robots learning about new items even after deployment. Uninitiated users can design algorithms that can handle the natural world's uncertainties by replicating the human brain's neuronal structure using neuromorphic computing.
The Loihi neuromorphic chip, one of the most significant architectures in the field, was created by Intel Labs. Around 130,000 artificial neurons make up Loihi, which communicate with one another through a neural network known as "spiking" (SNN). A variety of devices, including a smart artificial skin and an electronic "nose" that can detect explosive odors, had already been powered by the chips.