Neuromorphic Computing: The future of AI 

With the development of artificial intelligence (AI), it is expected to become a more important part of today’s edge technology market.

Known as AI of Things or AIoT, several processor vendors such as Intel and Nvidia have propelled AI chips for such low low-power environments with their Movidius and Jetson product lines.

Computing at the edge also contributes to lower latency than sending information to the cloud. 

A decade ago, the question arose of whether software and hardware could function in the same way as a biological brain, including incredible energy efficiency. Based on the plethora of sci-fi movies, and the onslaught of conspiracy theories, the answer seemed to be a yes, but only in the fictional world. 

But today, the answer seems to be a definitive YES! Cue, Neuromorphic Computing. 

There are several applications and even more potential for it. You can explore this phenomenon in depth, all you need is good internet. And if you’re searching for a new Internet Service Provider (ISP) then we suggest shifting to Cox. In addition to reasonable prices, their representatives are available around the clock at the Cox customer service number

Given the vastness of this topic, it is challenging to put everything in one post, but let us tell you what it is, how it works, and how you can apply it and make your sci-fi dreams become a reality.

What is neuromorphic computing?

Neuromorphic computing essentially combines artificial neurons to operate on the principles of the human brain. 

The work of neuromorphic computing begins with the installation of artificial neural networks, consisting of millions of artificial neurons. 

These neurons are similar to neurons in the human brain. Layers of these artificial neurons signal each other, allowing the device to function like a human brain. These electrical signals convert inputs into outputs, which is how the machines work.

Unlike traditional computers, where there are only two possible options, there are several options for calculating when the receiving computer neuron is activated in some way. 

Neuromorphic chips may be more energy efficient, especially for complex tasks, because they are able to transfer the gradient of understanding from neuron to neuron and make them work together at the same time.

How does it even work?

Neuromorphic Computing creates spiked neural networks. Spikes from individual electronic neurons fire other neurons in a cascade that mimics the physics of the human brain and nervous system. 

This works in a similar way to how the brain sends and receives signals from neurons that activate computations. 

Traditional neural networks and machine learning calculations are well suited to modern algorithms. They tend to prioritize achievement or often leading to one at the expense of the other. 

In contrast, neuromorphic systems offer both high processing power and low power consumption. 

Applications of Neuromorphic Computing

While this phenomenon is still relatively and undergoing developments, there are some applications, potential and actual, that have been identified. 

  1. Healthcare

Neuromorphic entities are extremely efficient in receiving and responding to data from their environment. When combined with organic materials, these devices become compatible with the human body. In the future, neuromorphic devices could be used to improve drug delivery systems.

Their highly responsive nature allows them to release the drug when they sense a change in body condition (such as different levels of insulin and glucose). A computer that behaves like a human brain would have the processing power to simulate something as complex as a brain, such as detecting diseases like Alzheimer’s.

Neuromorphic computing devices can also be used in prosthetics. Again, their ability to efficiently receive and process an external signal is an advantage.

Using neuromorphs neuromorphic instead of traditional devices could create a more realistic and seamless experience for people with prosthetics.

  1. Business Operations

Elements of large projects and product modifications can also benefit from the use of neuromorphic computing. It can be used to easily process large datasets from environmental sensors. 

These sensors can measure water content, temperature, radiation, and other parameters according to the needs of the industry.

The neuromorphic computational framework can help recognize patterns in this data, making it easier to draw effective conclusions.

  1. Customizing Products

Neuromorphic devices can also contribute to product customization due to the nature of their building materials. 

These materials can be converted into fluids that are easy to manipulate. In liquid form, they can be processed through additive manufacturing into devices specifically tailored to user needs.

  1. Imaging

Neuromorphic vision sensors produce images similar to the human eye. 

These imaging devices are event event-based. 

This indicates that they respond to light intensity (external signal) and not to an internal signal when forming an image. In addition, their speed does not depend on the usual frame rate. 

The combination of these mechanisms provides a much more efficient use of data. These sensors also don’t experience motion blur or environmental lag like their traditional counterparts. These properties could make neuromorphic vision sensors a welcome addition to virtual and augmented reality technologies.


Now we can certainly say that neuromorphic computing will be the next trend. So we need to grab our popcorn and catch up with the industry. The computer market is growing beyond our imaginations!