Neuromorphic Computing: What Is It?

The next big thing in AI innovation may be human-thinking computers.

Euromorphic computing is the process of designing and building computers so that they closely resemble the composition and capabilities of the human brain.

Neuromorphic computers mimic the way human brains process information by using artificial neurons and synapses. This enables them to solve problems, identify patterns, and make decisions more quickly and effectively than the computers we use on a daily basis.

Andreea Danielescu, an associate director at the digital research company Accenture Labs, said, "It's brain-inspired hardware and algorithms."

Neuromorphic computing is a relatively young field. Beyond the research being conducted by academic institutions, governmental organizations, and big tech firms like IBM and Intel Labs, it has very few practical uses. Nevertheless, neuromorphic computing has a lot of potential, especially in fields where efficiency and speed are crucial, such as edge computing, driverless cars, cognitive computing, and other applications of artificial intelligence.

According to Kwabena Boahen, a neuromorphic computing scientist and professor at Stanford University, the size of the greatest AI computations nowadays doubles every three to four months. Numerous scientists think that Moore's Law, which only doubles every two years, might be circumvented with the help of neuromorphic computing.

Tech analyst Daniel Bron told Built In that "AI is not going to progress to the point it needs to with the current computers we have." "The operation of AI is far more efficient on neuromorphic computing. Is it required? I'm not sure if it's required just now. However, it is unquestionably far more effective.

What Is the Process of Neuromorphic Computing?

You must first comprehend the cognitive processes that neuromorphic computing aims to replicate in order to comprehend how it functions.

According to Bron, neuromorphic designs are most frequently based on the brain's neocortex. Higher order cognitive processes like language, motor control, spatial thinking, and sensory perception are assumed to take place there. The extensive interconnection and layered structure of the neocortex play a crucial role in its capacity to handle complicated information and facilitate human thought.

The neurons and synapses that make up the neocortex transmit and receive information from the brain incredibly quickly and efficiently, almost instantaneously. It is what instructs your foot to move right away in the event that you inadvertently walk on a sharp nail.

Computers that are neuromorphic attempt to match such effectiveness. By creating what are known as spiking neural networks, they do this. These are created when artificial synaptic devices that transmit electrical signals between spiking neurons—which store information as though they were biological neurons—are coupled.

An artificial neural network is a set of algorithms that operate on a standard computer and simulate the logic of a human brain. A spiking neural network is simply the hardware counterpart of this system.

The ways in which neural computing and conventional computing are different

Von Neumann architecture, the conventional computer design that is still widely used today, is not the same as neuromorphic computing architecture.

Information is processed by von Neumann computers in binary, which means that everything is either a one or a zero. Additionally, they are sequential by nature, clearly differentiating between memory storage (RAM) and data processing (on CPUs).

In the meanwhile, millions of artificial neurons and synapses can process many pieces of information at once on neuromorphic computers. Compared to von Neumann computers, this offers the system a lot more computational alternatives. Increasingly tightly integrating memory and processor also allows neuromorphic computers to accelerate increasingly data-intensive operations.

Since von Neumann architectures are energy inefficient and frequently encounter data transfer bottlenecks that impede performance, researchers are pursuing alternative architectures such as neuromorphic and quantum. Von Neumann architectures have been the industry standard for decades and are used for a wide range of applications, from word processing to scientific simulations. However, as time goes on, these architectures will become more and more difficult to deliver the increases in compute power that we need.

Comparing Neuromorphic and Quantum Computing

 Neuromorphic computing: is an emerging field in computing that has its own unique features, benefits, and uses. It is inspired by the structure and functions of the human brain; it uses artificial neurons and synapses to achieve parallel processing and real-time learning; it works well for tasks involving pattern recognition and sensory processing; it is logistically easier to implement than Quantum computing; and it uses less energy than Quantum computing.

Quantum Computing

·        Uses information processing techniques based on quantum mechanical concepts;

·        Operates and resolves multidimensional quantum algorithms using qubits, or quantum bits;

·        Is particularly adept at quickly and effectively resolving challenging issues like molecular modeling and cryptography;

·        Compared to neuromorphic computers, demands lower temperatures and more power.

Even though they are extremely distinct from one another, both quantum and neuromorphic computing have a lot of potential and are still in the very early phases of research and development.

Neuromorphic Computing's advantages

Given its many advantages, neuromorphic computing is positioned to revolutionize the field of advanced computing.

NEUROMORPHIC COMPUTING BENEFITS

·        Operates more quickly than traditional computing

·        Proficient in identifying patterns

·        Able to pick things up fast

·        Energy-conserving

EXCIERTING CONVENTIONAL COMPUTING

By more precisely mimicking the electrical characteristics of actual neurons, neuromorphic devices may be able to reduce processing time and energy consumption. Additionally, neurons can produce replies "pretty much instantly" because to their event-driven operation, which means that they only receive information when pertinent events take place, according to Alexander Harrowell, a principal analyst at tech consultancy Omdia, who spoke with Built In.

Low latency is usually advantageous, but in technology such as Internet of Things devices that rely on real-time processing of sensor input, it can have a significant impact.

OUTSTANDING IN PATTERN RECOGNITION

Neuromorphic computers are very adept at identifying patterns because of their massively parallel information processing. As a result, Danielescu of Accenture Labs stated that they are also adept at spotting irregularities, which can be useful in anything from cybersecurity to health monitoring.

CAPABLE OF QUICK LEARNING

Similar to humans, neuromorphic computers are made to learn in real time and adjust to changing inputs by altering the strength of the connections between neurons in response to acquired knowledge.

According to Bron, "Neural networks are made to adjust constantly." "They are designed to evolve and improve continuously, enabling it to get better and better.”

This adaptability can be useful in situations where rapid decision-making and ongoing learning are required, such as when training a robot to work on an assembly line or when allowing cars to drive themselves through a congested city street.

POWERFUL ENERGY

The energy efficiency of neuromorphic computing is one of its main benefits; this might be especially helpful in the artificial intelligence business, which is known for being inefficient.

Instead of having distinct sections for each, as von Neumann designs do, neuromorphic computers can process and store data jointly on each individual neuron. The ability to complete numerous tasks at once thanks to parallel processing can result in decreased energy usage and faster task completion. Additionally, because spiking neural networks only process in reaction to spikes, only a tiny percentage of a system's neurons are ever powered on at any given moment, with the remainder remaining inactive.

The Difficulties of Neuromorphic Processing

Although neuromorphic computing has great promise for transforming artificial intelligence applications, data analysis, and even our comprehension of human cognition, its advancement is confronted with various obstacles.

·        Lacks common standards for evaluating success

·        Restricted availability of software and hardware

·        Challenging to understand and use

·        Lower accuracy and precision when compared to other neural networks of a same kind

Not based on benchmarks or standards

Since neuromorphic computing is still in its infancy, it is challenging to evaluate its performance and demonstrate its value outside of a research lab because there are currently no accepted standards for this technology. Furthermore, sharing applications and findings may be challenging due to the absence of standardized neuromorphic computing architectures and software interfaces. However, Danielescu stated that leaders in academia and business are making a "big push" to alter this.

Restricted Software and Hardware

One of the biggest challenges in hardware design and production is creating neuromorphic technology that can accurately simulate the complexity of the human brain. This is due to the fact that the von Neumann paradigm has mostly shaped the evolution of all accepted computing norms, such as data encoding.

Frame-based cameras, for instance, interpret and process visual input as a sequence of discrete frames. However, information such as changes in a visual field over time would be encoded by event-based cameras equipped with a neuromorphic processor. This allows you to detect motions far more quickly than you would on a conventional camera with von Neumann architecture, but in order to fully benefit from this, new generations of memory, storage, and sensory technology would need to be developed the neuromorphic apparatus.

Software is no different. The majority of neuromorphic computing that is done now uses algorithms and common programming languages that were created for von Neumann hardware, which may have limitations.

Diminished Precision and Accuracy

Spiking neural networks are not immediately mapped onto machine learning techniques that have proven successful for deep learning applications; instead, these algorithms need to be modified. This entails mapping a deep neural network to neuromorphic hardware, training it, and then turning it into a spiking neural network. This adaptability may result in a loss of accuracy and precision, as can the general complexity of neuromorphic systems.

According to Bron, "the appropriate software building tools don't really exist for these things." “Building for it is still very difficult.”

Applications of Neuromorphic Computing

Despite these obstacles, the subject of neuromorphic computing is nevertheless well supported; one estimate puts its value at $8 billion. And because of its exceptional capacity to replicate the brain's information processing and learning capacities, researchers are excited about its potential to completely transform a variety of IT disciplines.

·        Automatic transportation

In order to travel safely and prevent collisions, self-driving cars must make snap choices, which might demand a lot of processing power. Self-driving cars could be able to complete tasks more quickly and with less energy consumption than if they used traditional computers by utilizing neuromorphic hardware and software. This can reduce overall energy emissions and enable speedier roadside responses and adjustments.

·        Drones

Drones with neuromorphic computing might be just as alive as an organism, it is as receptive and reacting to airborne stimuli. With this technology, vision-based drones might be able to navigate tricky terrain or avoid hazards on their own. Additionally, a neuromorphic drone can be designed to only use more energy when it senses changes in its surroundings. This feature enables the drone to react quickly to unexpected situations, like military or rescue missions.

·        Edge Intelligence

Edge AI, where computations are done locally on a machine (like a smart device or autonomous vehicle) rather than in a centralized cloud computing facility or offsite data center, necessitates the real-time processing of data from things like sensors and cameras. This is where neuromorphic computing shines because of its energy efficiency, adaptability, and real-time data processing capabilities.

Neuromorphic computing's event-driven and parallel processing capabilities allow it to  Facilitate prompt and low-latency decision-making. Furthermore, because of their energy efficiency, these gadgets' batteries may last longer, lowering the frequency of edge device replacement or recharging around the house. According to some research, neuromorphic computing actually uses batteries 100 times more efficiently than traditional computer, according to Bron.

·        Mechanisms

Robots using neuromorphic systems will be able to perceive and make decisions more intuitively, navigate complicated situations (such as a factory floor), identify items, and engage with people more organically.

·        Fraud detection

Because neuromorphic computing is so good at identifying complicated patterns, it could be able to spot tiny patterns that point to fraud or security lapses, like strange spending habits or illegal or fake login attempts. Furthermore, neuromorphic computing's low latency processing could allow a swifter response once the fraud has been detected, such as freezing accounts or alerting the proper authorities in real time.

Research on Neuroscience

Neuromorphic computer hardware uses neural networks inspired by the human brain to improve our understanding of human cognition. Researchers may discover more about the inner workings of the brain as they attempt to replicate human mental processes in technology.

Intel and Cornell University collaborated in 2020 to practically train Loihi, an anthropomorphic computer chip, to recognize scents. The researchers eventually stated that in order to better understand how the brain's neural circuits resolve challenging computational issues, they would like to expand their methodology to include functions like sensory scene processing and decision-making.

Over the course of ten years, the Human Brain Project—an EU-funded consortium comprising about 140 universities, teaching hospitals, and research centers—tried to replicate the human brain using two neuromorphic supercomputers. It came to an end with its work in September of 2023