A human brain never fails to surprise! Can you believe this, it has a rare ability to process even the most complex information at a dizzying speed and the amount of energy used while doing so is pretty less. No wonder human brains are considered as a role model for the new age computing technologies. Several known scientists across the globe have stated that neuromorphic computing is the next big thing in computational technology growth. So in simple words, everything is changed, the way we process information, and solve a bunch of comprehensive tasks, etc. As the title suggests, the following post emphasises on what neuromorphic computing is, how these technologies work, and most importantly of all, how they are inspired by human brains.
Introducing Neuromorphic Computing
It has been decades, and finally, the moment has arrived. Experts have been working for years to develop brain-inspired computer hardware; however, we haven’t reached the breakout moment yet. So what is neuromorphic computing? Well, it is a method used by computer engineers. Here lots and lots of elements are used and all these elements are created on the basis of a human brain and nervous system. Some of the common disciplines included here are computer science, biology, mathematics, electronic engineering and physics. This significantly leads to the creation of bio-inspired computer systems and hardware.
Neuromorphic computing is basically an interdisciplinary field. A field where you find best of all the worlds including neuroscience, computer science, and electrical engineering. The term itself means brain-like, so in layman language, here a hardware is developed which surely operates pretty much like human brain, it thoroughly mimics it in terms of behavior and functions. So what’s the point of introducing such technology? Well, you can create more efficient computing with lower energy consumption and higher speed, especially in comparison to ordinary computers now.
The term neuromorphic means “brain-like” computing systems, which are designed and assembled in a way that they thoroughly mimic the structure and all the functions of a human brain. Basically, the ultimate objective is to develop hardware which operates in a similar manner to neural networks in biological organisms, enabling efficient computing.
History says the concept emerged back in the 1980s. The term was first coined by American scientists named Carver Mead while working on VLSI systems. He was a firm believer in the fact that the computational abilities of the brain. So, in other words, these systems are far better than the traditional ones, especially in conducting tasks such as image recognition and sensory data processing.
The first generation mainly used analogue circuits to specially mimic the behaviour of neurons as well as different synapses in the brain. So why was this needed in the first place, it was to perform real-time computations, which were highly preferred in robotics and sensory data processing. Unfortunately, these systems became easily prone to variability and intense noise, leading to lowering scalability and reliability.
After a while, the second generation came in 2000, where digital circuits were considered to successfully stimulate the spiking behaviour of neurons; this led to amazing precision and high scalability in comparison to analog counterparts.
Next came the 3rd generation computing systems, which featured integration of memory and processing within a single device; the concept was known as memristive. These devices incorporate phase-change memory and resistive random-access memory. In simple words, all the information is processed within the same location and not just that, all the neurons as well as synapses are the same which are of the human brain. So high energy consumption is reduced and increased computational efficiency.
Time to Understand Its Working
I am sure till now you must have found this concept pretty difficult to understand. So here let me delve into its working so that you can have a better perspective regarding the same.
The working begins with computing devices featuring effective placement of Artificial Neural Networks (ANN) comprising millions of artificial neurons and they are pretty similar to human neurons.
Imagine considering a machine to successfully act and work like a human brain. Here several layers are incorporated to pass signals successfully to one-another. So what these signals do, they successfully convert input into output which result in successful working of neuromorphic computing devices.
The passing of electric spikes or signal functions is mainly due to Spiking Neural Networks (SNN). So what this architecture assists in, it allows artificial machines to work much like any human brain and performs functions pretty much like the human brain does that too on a daily basis.
Right from visual recognition to absolute interpretation of data, the list is too long. Now artificial neurons mainly consume power, especially when electric spikes are successfully passed through them, and do you know what is the best part here, these neuromorphic computing machines are low-power-consuming computers in comparison to traditional computers.
So other than working like a human brain, neuromorphic computing has definitely advanced developments in the field of technology. Earlier, computing systems required lots and lots of space to function. However, today it no longer seems to be the case, and things are happening much faster and better.
Top-rated Features of Neuromorphic Computing
Rapid Response System
These computers are pretty famous for their rapid response systems. Do you know why? Because their processing is extremely fast, especially in comparison to traditional computing devices, these ones are developed to successfully work like a human brain and this is the reason why their rapid response is a major highlight.
Lower Consumption of Power
The concept of Spiking Neural Networks (SNN) which assists neuromorphic machines to work seamlessly especially when the electric spikes or signals are successfully passed via artificial neurons. These neurons mainly work when electric spikes are successfully passed via and this does consume low energy.
High Adaptability
It may quite interest you to know that modern computers have a knack of successfully adapting and neuromorphic computing is no longer an exception. And since these devices have higher adaptability, neuromorphic computing works significantly with evolving demands of technology.
Not to mention, these neuromorphic computers adapt themselves and change from time to time resulting in efficient working.
Fast-learning Pace
Another feature that makes neuromorphic computing a cut above is that it is highly fast-paced especially in terms of learning. All the algorithms are developed based on absolute interpretation of data and can be successfully formulated as soon as the new data is fed, neuromorphic computing enables machines to learn rapidly.
Mobile Architecture
Another striking feature is mobile architecture. Earlier traditional computers used to consume vast amounts of space for working. Well, that’s not the case anymore, in today’s times these neuromorphic computing devices have become mobile and handy and they no longer require much space and turn out to be highly efficient. So less space occupancy.
Final Words
We all know that the current artificial development projects heavily rely on graphics processing units (GPUs). These hardwares were meant to render graphics in video games. Today, they are highly recommended in AI-based tech where any kind of complex calculation is well taken care of and at the same time large amounts of data is being processed. This is one of the core reasons why neuromorphic computing is highly recommended with AI.
However, have you ever come across the saying that no power comes without a cost. GPUs consume lots and lots of energy, so if you are working in an environment where being energy efficient is a must then you need to look for other options such as neuromorphic computing that surely saves energy.
Lastly I would like to conclude the post by mentioning potential use cases of neuromorphic computing.
- Image and video recognition – Such kinds of systems can be trained especially to recognize relevant patterns especially among different objects and images and videos. For example – surveillance, self-driving cars, and medical imaging.
- Robotics – The concept is highly appreciated in computing to develop more adaptive and intelligent robots. These bots tend to learn from the current environment and perform complex tasks with much ease and greater efficiency.
- Edge AI – Have you wondered why Neuromorphic computing is considered an ideal source for developing AI apps? Since lower power consumption and real-time processing is critical, more and more IoT devices tend to trend towards the concept.
- Fraud detection – Of course, the ever-increasing security breaches shouldn’t be ignored at any rate. This technology is highly recommended to detect fraudulent activities by successfully recognizing all the unusual patterns within the transaction and ensuring early and accurate fraud detection.
- Neuroscience research – Here you are bound to receive a platform where it is possible to stimulate as well as study brain functions, so what happens next is, you are able to gain a better understanding of neurological disorders and at the same time several treatments can be taken care of.
On and all, the concept possesses high versatility and potential benefits irrespective of different fields and industry verticals. So are you ready to take a significant leap forward with the help of AI and neuromorphic computing?
So that’s all for now! I hope you did find the following post worth taking into account. In case if you have any doubt or query, feel free to mention them in the comment section below. Also keep watching the space to have a better idea on neuromorphic computing.
