Neuromorphic Computing
A team of scientists from Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR) has developed Artificial Synapse for Brain-Like Computing or Neuromorphic Computing.
They have used scandium nitride (ScN), a semiconducting
material with supreme stability and Complementary
Metal-Oxide-Semiconductor (CMOS) compatibility, to develop brain-like
computing.
What are the Significances of the Study?
Neuromorphic hardware aims at mimicking a biological
synapse that monitors and remembers the signal generated by the
stimuli.
ScN is used to develop a device mimicking a synapse that
controls the signal transmission as well as remembers the
signal.
Significance:
This invention can provide a new material for stable, CMOS-compatible
optoelectronic synaptic functionalities at a relatively lower energy cost and
hence has the potential to be translated into an industrial product.
The traditional computers have physically separated
memory storage and processing units. As a result, it takes enormous
energy and time to transfer data between these units during an operation.
On the contrary, the human brain is a supreme biological
computer that is smaller and more efficient due to the presence of a synapse (the
connection between two neurons) that plays the role of both processor and
memory storage unit.
In the current era of artificial intelligence, the brain-like
computing approach can help meet the escalating computational demands.
What is Neuromorphic Computing?
Inspired by the human brain and the functioning of the nervous
system, Neuromorphic Computing was a concept introduced in the 1980s.
Neuromorphic Computing refers to the designing of
computers that are based on the systems found in the human brain and
the nervous system.
Neuromorphic computing devices can work as efficiently as the
human brain without acquiring large room for the placement of software.
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).
Working Mechanism:
The working mechanism of neuromorphic computing involves the use
of Artificial Neural Networks (ANN) made up of millions of artificial
neurons, similar to those in the human brain.
These neurons pass signals to each other in layers, converting
input into output through electric spikes or signals, based on the
architecture of Spiking Neural Networks (SNN).
This allows the machine to mimic the neuro-biological
networks in the human brain and perform tasks efficiently and
effectively, such as visual recognition and data interpretation.
Significance:
Neuromorphic computing has opened the doors to better
technology and rapid growth in computer engineering.
Neuromorphic computing has been a revolutionary concept
in the realm of Artificial Intelligence.
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.