A concept of computer engineering, Neuromorphic Computing refers to the designing of computers that are based on the systems found in the human brain and the nervous system.
Driven by the vast potential and ability of the human brain, neuromorphic computing devises computers that can work as efficiently as the human brain without acquiring large room for the placement of software.
Inspired by the human brain and the functioning of the nervous system, Neuromorphic Computing was a concept introduced in the 1980s. Yet this concept has taken the front seat in recent times as Artificial Intelligence has led scientists to advance Neuromorphic Computing to excel in the field of technology.
One of the technological advancements that has rekindled the interest of scientists in neuromorphic computing is the development of the Artificial Neural Network model (ANN).
Since traditional computers, backed by CPUs (Computer Processing Units) do not have the ability to support neuromorphic computing, modern computers are now being built with adequate hardware to support such technology.
Backed by the advanced technology of neuromorphic computing, computers can now act and work like the human brain. With the help of algorithms and data, neuromorphic computing enables computers to work rapidly and on low energy too.
While the definition of this concept can be a bit too complicated to understand, the working of neuromorphic computing can make you understand the essence of it more easily. Let's begin with the working of neuromorphic computing.
The working of neuromorphic computing-enabled devices begins with the placement of Artificial Neural Networks (ANN) that comprise millions of artificial neurons. These neurons are similar to the human brain neurons.
Enabling a machine (computer) to act and work like the human brain, layers of these artificial neurons pass signals to one another. These electric signals or electric spikes convert input into an output that results in the working of neuromorphic computing machines.
The passing on of electric spikes or signals functions on the basis of Spiking Neural Networks (SNN). This spiking neural network architecture further enables an artificial machine to work like the human brain does and perform functions that humans can do on a daily basis.
This can involve visual recognition, interpretation of data, and a lot more such tasks. Since these artificial neurons only consume power when the electric spikes are passed through them, neuromorphic computing machines are low-power-consuming computers as compared to traditional computers.
By imitating the neuro-biological networks present in the human brain, neuromorphic computing machines work like a human brain and perform tasks efficiently and effectively.
Bringing on the ability to work like the human brain, neuromorphic computing has advanced the developments in the field of technology. The engineering of computers in the earlier times led to the generation of traditional computers that consumed a lot of space for functioning.
However, computers working on the basis of neuromorphic computing consume much less space with an in-built capability to work faster and better.
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Neuromorphic computers are specifically known for their rapid response system because their processing is highly rapid. As compared to traditional computers, neuromorphic computers are built to work like a human brain and so their rapid response system is a major highlight.
Owing to the concept of Spiking Neural Networks (SNN), neuromorphic machines work when electric spikes or signals are passed through the artificial neurons. These artificial neurons work only when electric spikes are passed through them thus consuming low energy.
Modern computers have a knack for adaptability and so do neuromorphic computers. With higher adaptability, neuromorphic computers work well according to the evolving demands of technology. With changing times, neuromorphic computers adapt themselves and change from time to time resulting in efficient working.
Machines working on the principle of neuromorphic computing are highly fast-paced when it comes to learning. Establishing algorithms based on interpretation of data and formulating algorithms as and when new data is fed into such computers, neuromorphic computing enables machines to learn rapidly.
One of the most striking features of neuromorphic computing is its mobile architecture. Unlike traditional computers that used to consume vast space for working, neuromorphic computers are mobile and handy. They do not require much space and are highly efficient in terms of space occupancy.
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An essential realm of AI, neuromorphic AI computing is significant because of its advanced technology. Leading to the functioning of artificial computers like the human brain, neuromorphic computing has opened the doors to better technology and rapid growth in computer engineering.
Not only does it lead to rapid growth but neuromorphic computing chips have also revolutionized the way computers work. From the analysis of data to machine learning algorithms, computers can do almost anything today.
While neuromorphic computing was a concept introduced in the 1980s, it has only been brought into the limelight in recent times. With numerous neuromorphic computing applications in physics, data analytics, and numerical algorithms, the significance of this concept is unmatched.
Even though the concept has many challenges to face, it still is leading the revolution of making computers work along the lines of the human brain.
"We’ve seen a lot of progress in scaling and industrialization of neuromorphic architectures. Still, building and deploying complete neuromorphic solutions will require overcoming some additional challenges." (From)
Artificial Intelligence technology intends to impart human abilities in computers to make them work like humans. On the other hand, neuromorphic computing attempts to engineer computers that work like the human brain does. Comprising millions of artificial neurons that pass on electric signals to one another, neuromorphic computing has been a revolutionary concept in the realm of Artificial Intelligence.
By inducing the technology of information processing, neuromorphic computers have become the leaders of AI that, as many say, have resulted in the 3rd wave. The third generation of AI has led scientists to draw parallels with the human brain and its abilities like the interpretation of data and adaptation.
With the help of one of the techniques of AI, (machine learning), neuromorphic computing has advanced the process of information processing and enabled computers to work with better and bigger technology.
Thanks to AI, neuromorphic computing has reinvented its place in the field of technology and is pushing the limits of AI further. Intertwined with each other, neuromorphic computing and AI have a long way to go as both attempt to mimic human abilities and imitate them in computer software.
"Intel Labs is driving computer-science research that contributes to this third generation of AI. Key focus areas include neuromorphic computing, which is concerned with emulating the neural structure and operation of the human brain, as well as probabilistic computing, which creates algorithmic approaches to dealing with the uncertainty, ambiguity, and contradiction in the natural world." Intel- Neuromorphic Computing
In simple terms, Artificial Intelligence future is Neuromorphic Computing. Setting forth the third wave or era of AI, neuromorphic computing will take over the technological advancements of the field and become the driving force of artificial intelligence future scope.
While the current wave of AI is faced with a number of challenges like heavy processing hardware and software storage capacity, the third wave of neuromorphic computing in AI will most likely put a stop to these challenges and enable the human-like activities performed by computers.
Neuromorphic chips, being manufactured by big tech giants like IBM, will be the key factor in making computers function like the human nervous system.
"The neuromorphic computing market is poised to grow rapidly over the next decade to reach approximately $1.78 billion (around Rs11,570 crore) by 2025, according to a 10 April report by US-based Research and Markets. The reason is simple—the growing interest of companies in Artificial Intelligence, or AI, which can always do with more and more computing power." Neuromorphic computing the future of AI
To conclude, neuromorphic computing will bring forth the untouched capabilities of AI and will set a revolutionary example in the coming years.
The objective of neuromorphic computing is to make computers behave like a human brain and work along the lines of the human nervous system, and neuromorphic computing posits the engineering of computers in a way that comprises millions of artificial silicon neurons enabled to transfer electric spikes from one another.
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In the long run, this concept will gain more relevance and regard as it is all set to bring about the 3rd wave of Artificial Intelligence.
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