Neuromorphic Computing Explain in Simple Way

Intel displayed its Loihi 2 chip on Thursday. It is the second generation of a processor family that uses conventional electronics and human brain architecture to change the computing industry.

This new chip is an example of a new technology called neuromorphic computing.

WHAT IS NEUROMORPHIC COMPUTING

• It is a method of computer engineering where elements of a computer system resemble the human brain and nervous system.
• Engineers in this computing system draw inspiration from computer science, biology, mathematics, electronic engineering, and physics.

HOW DOES IT WORK?  

• It can function like a human brain because it works through a Spiking Neural Network (SNN), a system consisting of arterial neurons and synapses that can process information spatially and temporally, just like our brain.  
• Electronic signals are transferred using an analogy circuit. Due to this circuit, the system can control how much electricity is flowing through the SNN.

• Neuromorphic systems are either digital or analogies; the role of synapses is played either by the software or memristors.
•  Memristors also have the synapses’ ability to store information and transmit it. They can keep a wide range of values instead of only one and zero, allowing the system to mimic the variation in the strength of a connection between two synapses. 

The weights in the artificial synapses in Neuromorphic computing can be altered to allow the brain-based systems to learn. 

The system includes phase-change memory, resistive RAM, spin-transfer torque magnetic RAM, and conductive bridge RAM. 

Researchers are looking at different ways to model the brain’s synapse, such as quantum dots and graphene.

ADVANTAGES OF NEUROMORPHIC COMPUTERS?

Neuromorphic Computers can perform complex calculations faster, more efficiently, and on a smaller footprint than traditional non-Neumann architectures.

• Information is stored on the entire network and not on a database. The disappearance of information in one place does not cause network disruption.
• It has a high fault tolerance. Can provide results even if one of its components fail.
• Can learn from past events.
• Has a high degree of elasticity and plasticity
• They are the most appropriate platform for deploying machine learning algorithms.
• These systems use AER (Address-Event-Representation), a communication protocol for conveying spikes between bio-inspired chips. This approach delivers the benefits of hard-wired connections between neurons without any wiring. Another advantage of this is that an  incoming signal can flow through the processor in real-time. While the irrelevant information  will be taken care of by the neural pipeline.
• Neuromorphic chips can do things like spotting sensory inputs like gestures, sounds, and smells.  
• Can respond to events based on changing environmental conditions. Only the parts of the computer in use will require power.

DISADVANTAGES OF NEOMORPHIC COMPUTERS?

• Require processors will parallel processing power that adheres to their structure. Realization of equipment is necessary.
• Does not give a reason like why or how when it comes up with a solution to a problem
• There is no clarity on a structure. The network structure is determined through trial and error.

CHALLENGES 

• While this Neuromorphic computing system has many benefits, there are also many challenges to consider while using this kind of technology. The number one challenge is public perception. 
• In a survey conducted by the European Commission, over 60% of EU citizens felt uncomfortable about using robots to take for their elderly parents and children.
• The field of neurocomputers is currently dominated by hardware developers, neuroscientists and machine learning researchers. To make neuromorphic systems a reality, it is necessary to look beyond the von Neumann framework.
• The system needs to be made in such a way that it is understandable to non-experts.

WHERE ARE NEUROCOMPUTERS BEING USED PRESENTLY?

•  In the Tianjin chip, which is being used to power a self-driving bike 
•  Intel’s Loihi chips
•  Intel’s Pohoiki Beach computers
•  IBM’s True North chip
•  Brain Scales from Heidelberg University, hybrid systems which combine biological experiments with computational analysis to study brain information processing. 

CONCLUSION

Neuromorphic computers are the next stage in computer evolution. Research has predicted that Neuromorphic computing will reach USD 550,593 thousand by 2027, at a CAGR of 89.1% between 2021 and 2027.

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