Neuromorphic Computing and Biomimetic AI Beyond 5G Hardware

The hardware-based computer that we use in our daily tasks is unable to handle the processes that our brain can. So, to solve this problem, the data centers, hardware developers, and programmers are making efforts. They are researching how to change that hardware-based computer. Neuromorphic computing is a field of technology working to combine mathematics, electrical engineering, biology, and computer science. The combination of these four fields can design an advanced artificial neural system. This system is capable of processing loads and sensing like our brain. 

What is neuromorphic computing

Neuromorphic computing is not too new technology, but most of the readers are still unfamiliar with this field. We will define and explain this technology here. This is the process of computer engineering that modeled the computer’s elements the same as systems in our brain and nervous system. It arranges artificial neurons to work on the rules of the human brain. This term is associated with both software and hardware computing elements. 

Neuromorphic computing works on SNNs, “The Spiking Neural Networks”. In SNNs, every single neuron transfers signal to other neurons independently. This technology copies the network of natural human neurons. 

Why is it better?

Neuromorphic hardware shows better performance for neural networks. Neural networks use real numbers like 0.242341 for representing values and weight of the structure of the neural network. But we need to transfer those values in binary to operate on our hardware-based computer. So, this process of transformation requires additional operations for computing the neural network. More operations are needed for development and training. 

If we use neuromorphic hardware, then there is no need for binary. Because neuromorphic hardware uses the real values in the form of voltage or current. For example, the number 0.242341 will be represented in 0.242341 volts. All this process occurs in the circuit. The binary value is not present during the process. The calculation also does not require too much time. The whole calculations occur at the speed of the circuit. 

We know that neural networks are used in the field of artificial intelligence. So, the main purpose of artificial intelligence is to recreate human thinking, behavior, and other tasks. Artificial intelligence and neuromorphic computing are working on the same method for the same purpose. Both these technologies have a similar aim to reproduce them and replicate human intelligence. However, Artificial intelligence encircles neuromorphic engineering and computing and envelops many different facts of technology. 

If we check the current condition of technology, the properties of artificial intelligence and neuromorphic computing have a limited range of capabilities to do a task. However, Moore’s law encourages these technologies and enables them to function like a human brain. 

Artificial intelligence hardware: the chips of neuromorphic computing

A perfect chip of neuromorphic computing copies the brain of a human that can be described as a unicorn. Different kinds of lessons are present that can teach lessons by working on neuromorphic computing. 

It is not too late to realize neuromorphic computing. It is a powerful method that costs less time. The technology of neuromorphic hardware is developing day by day.

Advantages

• It increases the workability by the use of artificial intelligence.
• It brings a lot of modern evolution as compared to our traditional methods. 
• This technology is energy efficient.
• Neuromorphic computing has a great speed of execution. It operates at a fast speed. 
• It increases the ability to learn new things. 
• The combination of neuromorphic computing and AI design robustness for controlling the local failure. 

Challenges:

• The biggest challenge that this technology may face is people’s perception. Parents may not agree to hand over their kids to a robot. 
• The technology is still under process, so we can’t make solid arguments about its progress. 

Conclusion:
AI and neuromorphic computing technologies are playing a great role to facilitate human beings. However, These technologies have the potential to change the concept of modernism. But people may respond less to robotics for their daily routine tasks. However, they can beat the challenges in the near future.