Huge Development in Neuromorphic Computing Market As New Applications Identified

Posted On April 25, 2022     

Neuromorphic Computers are a system that synthetically replicates the brain's logic. Like the brain, it works by energising tiny pin-like structures and adding minuscule charges released by surrounding sensors till a specific electrical level is attained. The pin then flashes a brief electrical burst, similar to a biological neuron, an action known as spiking.

A research team has demonstrated that these computers can solve more complex problems than artificial intelligence. In fact, they may even earn a place in high-performance computing. This is in contrast to the metronomical regularity via which the information is passed along in conventional computers. The study is highly relevant for Neuromorphic Computing Market as it showcases the relevance of the system in numerous applications.

The findings reveal that neuromorphic simulations utilise the statistical method known as random walks. Further, they can follow, among other things, X-rays moving through bone and soft tissue and disease spreading across a population. The list also includes information flowing via social networks and financial market movements.

In the present study, researchers demonstrated that neuromorphic technology could deliver computational advantages significant to numerous applications, not just artificial intelligence. Such applications include radiation transport and molecular simulations and computational economics, biology modelling, and particle physics.

The audacious claims should pique the interest of the high-performance computing community. Neuromorphic computers, they claim, will solve problems faster and consume less energy than traditional computers in ideal instances. This is because developing capacities to address statistical issues is a growing worry.

The scientists used the 50-million-chip Loihi platform to correctly replicate random walks of gaseous molecules diffusing past a barrier, a fundamental chemical problem. They then demonstrated that the technique could be expanded to more complicated diffusion processes.

The statements are not intended to question the supremacy of standard computing technologies used to run utilities, desktop computers, and phones. However, there are some sectors where computer speed and lower energy costs may make neuromorphic computing the ultimately preferable choice.

According to the group, the next generation of Loihi will expand its current chip scale from 128,000 neurons per chip to up to one million. On a larger scale, numerous chips are combined to form aboard.

Perhaps it seems logical that technology like Loihi could find its way into future high-performance computing platforms. This could help make HPC far more energy-efficient, environmentally friendly, and, in general, more economical.

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