Neuromorphic Computing and Neuron Spike for Speedy AI

Neuromorphic computing a neuron spike now leverages speedy AI

The neuronal structure of the human brain is emulated in neuromorphic computer research. The next generation of AI will extend AI into domains similar to human cognition, like interpretation and independent adaptability. This is important for overcoming the fragility of AI solutions based on neural network training and testing, which rely on literal, deterministic interpretations of events that lack perspective and common understanding. To automate everyday human operations, next-generation AI must be able to address unexpected circumstances and abstract.

Let's explore more about neuromorphic computing and neuron spikes in the sections below.

What is Neuromorphic Computing?

Neuromorphic computing is an engineering method that models computer components after principles found in the human brain and nervous system. The phrase refers to the development of both hardware and software computer components.

To build artificial neural systems found in biological architecture, neuromorphic engineers depend on a variety of fields, including computer science, biology, mathematics, electrical engineering, and physics.

Why are Neuromorphic Systems Needed?

Most modern hardware is built on the von Neumann architecture, which separates memory and computation. Von Neumann chips waste time and energy since they must shuttle information back and forth between the memory and the CPU.

Chipmakers have long been able to increase the amount of processing power on a chip by squeezing more transistors onto these von Neumann computers, thanks to Moore's Law. However, the challenges with reducing transistors much more, their energy needs, and the heat they emit imply that without a change in chip principles, that won't be possible for much longer.

Von Neumann's designs will make it increasingly difficult to achieve the needed improvements in computational power as time goes on.

To stay up, a new non-von Neumann design will be required: neuromorphic structure. Both quantum computing and neuromorphic systems have been proposed as solutions, with neuromorphic computing, or brain-inspired computing, expected to be commercialized first.

A neuromorphic computer might channel the brain's functioning to solve various challenges in addition to perhaps bypassing the von Neumann barrier. Brains employ massively parallel computation, whereas von Neumann systems are mostly serial.

Computer like a Human Brain

Neurons, a kind of nerve cell, transport messages to and from the brain. When you tread on a pin, nerve endings in your foot's skin detect the damage and send an action potential (essentially, a signal to activate) to the neuron that's attached to the foot. The action potential leads the neuron to release chemicals across a gap known as a synapse, a process that repeats itself across numerous neurons until the signal reaches the brain. The pain is then registered by your brain, and signals are transmitted from neuron to neuron until the receiver receives your leg muscles – and you raise your foot.

An action potential can be activated by a large number of stimuli at once (spatial) or by the input that accumulates over time (temporal). Because of these mechanisms, as well as the massive interconnectedness of synapses (one synapse may be linked to 10,000 others), the brain can transport information rapidly and effectively.

Memristors might potentially be effective in simulating another valuable aspect of the brain: synapses' capacity to retain as well as transfer information. Memristors, which can hold a range of values rather than simply one and zero, can imitate the way the intensity of a connection between the two synapses can change.

Neuromorphic Systems Uses

For compute-intensive activities, edge devices like smartphones must presently delegate processing to a cloud-based platform, which executes the query and returns the response to the device. That question wouldn't have to be sent back and forth with computational models; it could be answered within the device itself.

However, arguably the most compelling reason for investing in neuromorphic computing is the potential it holds for AI.

Current AI is primarily rules-based, educated on datasets until it learns to create a certain output. However, that is not how the brain operates: our grey matter is far more at ease with ambiguity and plasticity.

It is believed that the next version of artificial intelligence will be able to cope with another few brain-like issues, such as constraint fulfillment, in which a system must discover the best solution to a problem with many constraints.

Neuromorphic systems are also more inclined to aid in the development of better AIs since they are more at ease with other sorts of issues, such as probabilistic computing, which requires systems to deal with noisy and uncertain input. Others, such as determinism and non-linear thinking, are still in their infancy in neuromorphic computer systems, but once confirmed, they have the potential to dramatically extend the applications of AI.

Spiking neuron: Faster and Accurate AI

DEXAT is a novel spiking neuron model developed by researchers at IIT-Delhi, led by Prof Manan Suri of the Department of Electrical Engineering (Double EXponential Adaptive Threshold). The discovery is crucial because it will aid in the development of accurate, quick, and energy-efficient neuromorphic artificial intelligence (AI) systems for real-world applications such as voice recognition.

The effort is multidisciplinary, straddling AI, neuromorphic hardware, and nanoelectronics.

"We've demonstrated that memory technology can be used for more than just storage. We've successfully employed semiconductor memory for in-memory computing, neuromorphic computing, sensing, edge AI, and hardware security. "In a news release from IIT-Delhi, Suri adds, "This study especially exploits analog characteristics of nanoscale oxide-based memory devices for generating adaptive spiking neurons."

In comparison to previous state-of-the-art adaptive cut-off spiking neurons, the study revealed a neuron model with greater accuracy, faster convergence, and versatility in hardware implementation. With fewer neurons, the suggested method provides excellent performance.

The researchers were able to effectively show a hybrid nanodevice-based hardware realization. Even with very significant device variability, the described nanodevice neuromorphic network was reported to attain 94% accuracy, showing resilience.

Conclusion

Spiking neural networks are used in neuromorphic computing to simulate how the brain operates. Traditional computing relies on transistors that are either on or off, one or zero. Spiking neural networks may communicate information in the very same spatially and temporally manner as the brain, resulting in more than one of two signals.