The global neuromorphic computing trends are set to dominate computer systems in 2022

Neuromorphic computing is a term referring to both hardware and software computing elements. It is a method of computer engineering in which elements of a computer are modelled after systems in the human brain and nervous systems.

Neuromorphic computing is drawn from several disciplines like computer science, biology, mathematics, electronic engineering, and physics to create an artificial neural system inspired by the biological structure. The two main goals of this technology is to create a device that can learn, retain information similar to the human brain; the other is to acquire new information proving a rational theory.

The key advantages of neuromorphic computing when compared to traditional approaches are it is energy efficient, has a rapid executive speed, robustness against local failures, and the ability to learn and process easily. The biggest driving force for investments in neuromorphic computing is the promise it holds for AI. The generation of AI tends to be based on rules, trained on datasets until it learns to generate an outcome.

Neuromorphic computing trends to adopt:

VLSI circuits: Very Large Scale Integration is the process of creating an integrated circuit by combining millions of MOS transistors into a single chip.

Controlling robotic platforms: Control systems using an array of neuromorphic computing elements can build and control the movements of robots.

Neural computation: It disseminates important, multidisciplinary research, in theory, modelling, computation, and statistics.

Remote sensing: Neuromorphic computing can be used for remote sensing purposes, for detecting and monitoring the physical characteristics of an area.

Intelligence analysis: It is a process by which the information collected about an enemy is used to answer questions and predict behaviours.