AI is rapidly transforming the everyday life of various fields by implementing innovative solutions and improving performance outcomes. Along with this, AI technology itself is advancing, surprising us with new achievements. However, in this field, AI faces challenges, as in some cases, progress is limited by technological aspects — processor power, i.e., computing speed and energy consumption. Here, all eyes turn to one alternative — neuromorphic computing, which we will discuss in this article.

What is Neuromorphic Computing?

In basic, this computing model is based on the aspect that it is computing that mimics the functioning of the human brain. This means that such computing can imitate the neural and synaptic structures and functions of the brain, resulting in specific hardware or software thinking like a human.

This field is becoming more widely discussed, but it is not entirely new. Misha Mahowald and Carver Mead laid the foundations for this technology in the 1980s by creating the first silicon retina, silicon neurons, and synapses, which attempted to achieve the ideal operation based on the replication of human brain activity.

Why is Neuromorphic Computing Significant?

As mentioned, with the increasing development of AI and its applicability, traditional methods will soon no longer be sufficient, leading to a growing demand for more powerful computing methods. Meanwhile, neuromorphic computing is one of the options that can help accelerate AI growth — offering greater computational performance and serving as one of the potential components of artificial superintelligence.

The Main Principles of Neuromorphic Computing

Generally, this science is based on biology or neurology, as they have direct connections to human physiology and functioning. We all understand that neurons are important nerve cells in humans that transmit information through impulses, allowing for the performance of and problem-solving in the most complex tasks. Similarly, in this science, neuromorphic computing systems are modelled to operate in spiking neural networks (SNN).

Spiking neurons and synapses form a spiking neural network. They store and process data, having their own values — charge and delay thresholds. Information moves between neurons through paths created by synapses. Synapses also have delay and weight values that correspond to the charges of neurons. Such charges describing the delays of neurons and synapses, as well as neuron thresholds or synaptic weights, are programmed throughout the computing system.
In short, during computation, the structure and functions of brain neurons and synapses are simulated, and an artificial nervous system is created.

What Value Does the Neuromorphic Computing Method Bring?

Traditional AI models are based on algorithms and mathematical calculations. Neuromorphic computing relies on specialization in neuromorphic chips that imitate the behaviour of neurons and synapses and retain their state when the power is turned off, eliminating the need for constant memory updates.

This computing method also brings various advantages. One of them is adaptability, where the system dynamically responds to the environment, stimuli, and learns in real-time, enabling new challenges to be qualitatively solved. Additionally, complex tasks can be addressed with lower energy consumption, and different operations can be processed in parallel, as AI data processing is decentralized and occurs across several different nodes, allowing the same decisions to be made faster than with traditional models.

The Applicability of Neuromorphic Computing in Real Situations

Neuromorphic computing possesses significant potential as it can enhance various industrial sectors. It is promising in robotics, where the imitation of human behaviour is most clearly understood. Such computing solutions improve robots’ functionality, versatility in solving various problems, and interaction with the environment. The market for autonomous vehicles can also evolve with this technology, as more advanced computing methods enhance their real-time decision-making, resulting in higher quality and more reliable performance (Cory, 2024).

Final Thoughts

Neuromorphic computing is becoming a new opportunity and perspective for AI advancement. Solutions based on this technology can become significantly higher quality when they are able to mimic human brain activity and make decisions as each of us does today.

If you are interested in this topic, we suggest you check our articles:

• Quantum Computing: Understanding Its Principles and Industry Applications
• AI-Powered Supercomputers: Advancing Technology and Innovation
• Nvidia’s New Era: Personal Supercomputers Are Coming To You

Sources: Hilaris Publishing SRL, IBM, Nature