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Neuromorphic Computing and Artificial Intelligence

Neuromorphic Computing
Neuromorphic Computing

Introduction:

Let’s discuss Neuromorphic Computing and Artificial Intelligence

As a matter of fact Neuromorphic computing is not a new concept. In the 1980s this term and concept were developed by Carver Mead. The term is also known as neuromorphic engineering. And Neuromorphic engineering generally describes analog, mixed-mode, digital and software systems. All of these systems tend to imply a neural system model. The basic concept of neuromorphic computing is to know how we can create architectures of desirable computations from individual neurons morphology, circuits and applications. In fact, neuromorphic computing takes inspiration from other subjects like physic, mathematics, and also biology, electronic engineering and computer science to design artificial neural systems. There are many practical examples of this approach in which neural systems are designed using artificial intelligence like eye-head systems, auditory processors, vision systems and autonomous robots. The strategy, principles and physical architecture of the above-mentioned system dependent upon biological nervous systems of neuromorphic engineering.

The increasing popularity of Neuromorphic Computing

Nowadays, increasing the popularity of these neural networks, deep learning, and neuromorphic computing-based systems have sparked a race to create advanced equipment of AI for neural network computations. And In the last few years, Neuromorphic Engineering has appeared as a thrilling research area, mainly due to the paradigm shift from traditional computing designs to cognitive, data-driven computing. Especially, in the field of AI neuromorphic plays an important role.

How we can combine AI and
neuromorphic computing?

Artificial neural networks (ANNs) are considered as the most important advancement in the field of artificial intelligence. Artificial neurons and small computation units are the building blocks of neural networks. The purpose of these building blocks can be implemented easily by mathematical functions. And Most of the times artificial neurons are not used alone but can perform appreciable tasks when stacked in layers. Artificial neurons can perform various types of works like detecting objects and also converting voice from audio to text. Neural networks spread across dozens of layers and also can contain hundreds of millions of neurons.

Process of a deep learning model

During the training process of a deep learning model, programmers run lots of neural network examples along with the anticipated outcomes. As more and more data is analyzed these artificial neurons are adjusted by the AI model. Gradually the model becomes more precise for the task. And it has been designed for, such as flagging bank transactions or detecting cancer.

The growing popularity of neural
networks and deep learning is a blessing for the GPU manufacturers. Nvidia, a
computer hardware maker, has seen its stock price increase by many folds in
recent years.

However, GPUs also lack the physical architecture of neural networks, and also still need to emulate neurons in the program, making progress at a rapid speed.  The differences between neural networks and GPU’s produce a lot of inefficiencies, like lots of power consumption.

structure

The structure of neuromorphic chips are different from many processors available in the market. Their design is according to the structure of artificial neural networks. This neuromorphic chip contains numerous tiny computing units that correspond to an artificial neuron. But computing units in neuromorphic chips cannot exhibit very much distinct tasks like CPUs. They can just implement the mathematical function of a single neuron because they don’t have enough power to perform other functions. Neuromorphic chips have characteristics to establish a physical connection with artificial neurons. And These types of connections consists of biotic neurons and their networks called synapses to make neuromorphic chip like an organic brain.

Real strength of neuromorphic computers

The real strength of neuromorphic computers is the array of physically connected artificial neurons. So, the structure of a neuromorphic computer makes them very effective at running and training of neural networks. They can also run AI at a faster speed while consuming less power than alike GPUs and CPUs. Power consumption is already a challenge for AI so it is a very important feature of the neuromorphic structure. Low power consumption and smaller size of neuromorphic computers make them appropriate for practicing cases that require to run AI systems at the edge as opposed to the cloud.

The Fact of neuromorphic chips

In the neuromorphic chips, a number of neurons play an important role because these chips can be categorized by the number of neurons they comprise. A neuromorphic chip named “Tianjic Chip”, contained 40,000 artificial neurons and 10 million synapses and also it is used in the self-driving bike in an area of 3.8 square millimeters. The Tianjic Chip is 1.6-100x faster and consumes 12-10,000x less power than the GPU that is capable of running an equal number of neurons.

The human brain contains 100 billion neurons so 40,000 is a limited number of neurons, as much as the brain of a fish. Many applications used AlexNet which is a famous image classification network, and it has more than 62 million parameters and a language model named OpenAI’s GPT-2 contains more than one billion parameters. Nowadays many companies are using neuromorphic chips in different AI applications.

Examples

Some Examples are as follow:

Intel’s Loihi chips contain 131,000 neurons and 130 million synapses. The Pohoiki computer contains 8.3 million neurons and also it gives 1000x better performance and also it is 10,000x more energy-efficient than GPUs which is equivalent to it.

What impacts will occur on the AI market?

In today’s life, AI is the most demanding topic. AI the use of artificial networks is a widespread technique. And In ANN, neurons are connectin the way that they can function in a biological way like a human brain. It can be possible by creating a network of interconnected artificial neurons. And also these are very efficient in computer vision, speech recognition, and medical diagnosis. And A I applications are also found in the cybersecurity. Because of the advancement in technology cyber-attacks also increase at the same rate.

Neuromorphic chips

But neuromorphic chips are more efficient and can bring our devices one step closer to act like human brains. Some companies like Qualcomm, Intel and IBM are in a high-stakes contest to develop the first neuromorphic computer. In visual or auditory data, a neuromorphic system may detect patterns and also change its predictions based on what it learns. Intel scientist Charles Augustine’s research paper expects that neuromorphic chips can grip artificial intelligence tasks such as rational computation, adaptive artificial intelligence, detecting data, and associated memory. They’ll also use 15–300 times less power than the maximum use of CMOS chips.

This is important since today’s AI systems, like Siri and Alexa, rely on cloud-based computing to perform tasks like answering a verbal inquiry or order. Smartphones operate on chips that do not have the processing power to utilize the algo’s needed for AI, even if they would immediately drain the telephone’s battery.

Research and discussion:

According to the research and development in the neuromorphic computers, some feature developments of it are as follows:

  • Neuromorphic chips will be root in smart cell phones near the future.
  • the drive of IoT Growth Neuromorphic processors will have a significant part.
  • In the conveyance, space examination, manufacturing, and defense Neuromorphic chips will play an important role.

And In the near future, smartphones embedded with neuromorphic chips can monitor your activities i.e. (Where you go and what you do.) in order to provide help before you even need it.  If your device can observe the atmosphere in a similar fashion as you do, then your device will easily comprehend your aims and requirements.

Neuromorphic computers will enable interstellar spacecraft to maneuver self-sufficiently and while utilizing very little power. Neuromorphic chips will also enable satellites for investigation purposes.

The Neuromorphic chips use less energy than traditional CPU chips and also deliver better efficiency. The reason is that they are designed and also manufacture differently. Neuromorphic typically consists of millions of “neurons” that have the ability to pass information in any direction to other neurons through 256 million connections, called “synapses.” In this way, they do not require to pass information from one transistor to the next in a line of trillions of transistors.

Conclusion

Neuromorphic computing is very young and has tremendous potential and Breakthroughs are expected in less than 15 years. It will allow engineers to design robots that can act like human beings. The types of robots that will be able to navigate on their own, can reply to the spoken commands, and recognize their surroundings, they will work according to our instructions by using very low power and give very efficient results. This is because neuromorphic computer chips use very little energy as compared to ANNs.

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