Neural network algorithms are boldly ushering in a new era of artificial intelligence (AI) applications. Both disciplines of AI, machine learning and deep learning, use neural networks. Regardless of where a data center is located, each facility needs to be able to meet server and network requirements and successfully handle extra workloads. The increase in the use of AI applications has substantial improvement potential that could affect how a data center operates.
As you may have gathered, the human brain is the inspiration for artificial neural networks. Our brain contains billions of neurons. These neurons communicate with each other. This process creates complex decision trees. The resulting cognitive ability is a result of these intricate decision trees. When a human learns new things, new neurons are formed and therefore new connections are formed as well.
This is the underlying concept for artificial neural networks. They follow the same principle. In order to form an artificial neural network, data scientists supply training data and feed it into machine learning or deep learning algorithms. These algorithms then use the known data to create new neural networks. They use the data provided to learn.
Imagine you need to create a neural network that could process a simple command. For example, the ability to recognize dogs. Utilizing machine learning, data scientists create a model and then they supply the model with images of dogs. These images are also known as training data. In order to provide much needed details, each neuron would represent a specific quality and a specific weight. While the training process is running, the algorithms will recalibrate the weight of each neuron in order to improve the accuracy of the results. If the task is simple, it can be performed in a few hours. If the task is more difficult, it can take up to a few days to process. Once the training data is processed, a functioning neural network is created. A vital element of the speed and success of this system is the processing power of a computer.
Neural Networks Transforming Data Centers
Because of the advancement in neural networks, new ways have been discovered on how to utilize them. One of these ways is by using neural networks in data centers. They are used primarily in two different ways, creating new requirements to serve AI-based applications and they help data centers optimize their services.
Importance of GPUs
If a data center is interested in attracting and generating business related to AI, the role of GPU-based processing is important to understand. Graphics processing units, or GPUs, are more efficient at processing neural network algorithms. Because of this, there is a high demand for data centers that possess a large number of quality GPUs.
There are certain issues associated with neural network processing. Data center operators and managers need to be aware these. For example, certain GPUs from certain manufacturers have restrictions, NVIDIA has restrictions on certain graphics cards for use in a data center. When data centers are prepared for issues like this, it is more attractive to prospective new business. Not to mention, the facility will be better prepared to serve AI applications.
Data centers can become dependent on neural networks as an invaluable resource. For example, let’s imagine a data center is responsible for a Miami colocation. If there are multiple customers, it can be a challenge to predict their future workload. A neural network-based application is able to learn the workload trends and behaviors of each customer and help to manage internal resources more efficiently.
Neural network applications are great at locating and detecting abnormalities within servers or even networks. Because of this valuable feature, data centers can improve cybersecurity algorithms. As a result, they are better equipped to protect against any threats.
Another feature that benefits data centers is the potential to automate. Neural network applications can make it so the data centers of the future can be equipped and outfitted with robots able to perform maintenance tasks and repairs. The robots will be able to learn about the network equipment and servers from the neural network. This is another way efficiency is increased in a data canter.
There is an endless number of creative ways a neural network-based application can be used. Because of this, more and more companies will be adopting this technology in order to process text, image, video and voice data. Data centers are poised to take advantage of this influx of business. In order to do so, they have to be prepared to provide services to these types of customers. Additionally, data centers can benefit themselves by adding this technology to their internal operations. It will benefit both their efficiency and bottom line.