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What is Neuromorphic computing and what are its applications

ake logical adaptations like a human brain. In this new method of computer engineering

Written by prajith s · 2 min read >

What is neuromorphic computing

Neuromorphic computing is the next generation of AI that will extend it to learn and retain information and even make logical adaptations like a human brain. In this new method of computer engineering, hardware and software elements of a computer will be designed and engineered according to the same physics of computation used by the human nervous system and brain.

An artificial neural network program differs from neuromorphic computing as it runs under a normal computer that mimics the logic of how a human brain thinks. Neuromorphic computing will be ideal as a hardware version to run a neural network as a software version. A precise electric current has to flow across a synapse, or the space between neurons depending on the quantity and type of ion

Unlike traditional computers where there are only two possible options, more computational options will be possible when the receiving computer neuron is activated in some way. The neuromorphic chips could be more energy efficient, especially for complex tasks, as it has the ability to transmit a gradient of understanding from neuron to neuron and have them all working together simultaneously.

The materials that are used in existing computers will not be suitable for realizing the exciting potential of neuromorphic chips. The current between artificial neurons cannot be controlled by something like silicon as its physical properties makes it flow randomly all over the chip. A team at MIT has designed a neuromorphic chip by layering single crystalline silicon and silicon-germanium on top of one another. There will be an organized flow of ions when an electric field is applied to this device. The architecture of neuromorphic systems is advancing in a way that neurons on these chips learn as they compute.

Application of neuromorphic computing


Neuromorphic devices could be used in prosthetics and to improve drug delivery in the human body. Traditional prosthetic devices can be replaced with neuromorphic devices to create a seamless and realistic experience. Its highly responsive nature makes it capable of releasing a drug upon sensing a change in the human body. A computer that behaves like a human brain will have the computing power to simulate something as complicated as the brain, such as identifying diseases like Alzheimer’s.

Large Scale Operations

Neuromorphic computing can benefit large-scale projects by easily processing large sets of data from environmental sensors that could measure parameters like content, temperature, and radiation. It will be easier to reach effective conclusions as various patterns in the data can be recognized by the neuromorphic computing structure.

Product Customisation

The building materials of neuromorphic computers can be transformed into easily manipulated fluids to be used in product customization. In liquid form, they can be used in additive manufacturing to create devices fit for specific needs.

Artificial Intelligence

The field of neuromorphic computing will push to match the functionality of the human brain which has neurons that are extremely fast and energy-efficient in receiving, processing, and sending signals. As the brain’s ability to collect and apply information is a particular focus in the field of AI, it would be beneficial for the two fields to collaborate going forward.

Researchers in the UK say that a system called SpiNNaker, can be used to simulate the behavior of the human cortex. SpiNNaker, which stands for Spiking Neural Network Architecture, designed by a team at the University of Manchester is another leap in the performance of neuromorphic computing. The project took a different approach by using traditional digital parts like cores and routers that connect and communicate with each other in innovative ways. SpiNNaker has achieved a huge milestone in neuromorphic computing by matching the results with that of a traditional supercomputer. It is expected to achieve computing performance with higher speed and more complexity for less energy cost.

Artificial neural systems are created in neuromorphic computing by combining disciplines including computer engineering, electronics engineering, biology, mathematics, and physics. The Loihi project by Intel and TrueNorth’s neurons by IBM are some of the exciting projects that aim to revolutionize the computing system inspired by the human brain. These projects focus on having a better grasp on the functioning of the human brain, mimic biological systems using improved building materials, and optimizing neural algorithms with better hardware architectures.

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