Neural Networks: How Data Centers are Catering to Future DemandsJune 11, 2018
The progress of neural network algorithms is ushering a new age of artificial intelligence (AI) applications. Both machine learning and deep learning disciplines of AI use neural networks. The increase of these algorithms have implications for your data centers. Whether you have a data center in Los Angeles or Tokyo, your facility needs to be able to meet the server and network requirements and handle the extra workloads.
Basic Understanding of Neural Networks
The inspiration for artificial neural networks is the human brain. The brain has billions of neurons. The neurons communicate with each other and create complex decision trees. The human cognitive ability is the result of these decision trees. As a human being learns new things, new neurons are created and new connections are formed.
Artificial neural networks follow the same principle. To form an artificial neural network, data scientists feed training data to machine learning or deep learning algorithms. These algorithms use the known data to form neural networks. In other words, the algorithms use the input data to learn.
Suppose, you need a neural network that can recognize cats. In a machine learning scenario, data scientists will create a model and then feed the model with known cat images, also known as training data. Each node or neuron of the model would represent a particular quality and a certain weight. During the training process, the algorithms will recalibrate the weights of the nodes to improve the accuracy of the overall neural network results.
Depending on the complexity of the task, it can take a few hours or it can take days to process the training data and create a functioning artificial neural network. Computer processing power plays a vital role in forming these networks.
Changing Landscape of Data Centers due to Neural Networks
Neural networks are affecting data centers in two ways. It’s creating new requirements for data centers to serve AI-based applications. Also, AI-based applications help data centers optimize their own services. Here are some pointers to prepare for the future:
Rising Demand for GPU-based Processing
Any data center looking to attract AI-related businesses need to understand the importance of GPU-based processing in neural network applications. Graphics Processing Units (GPUs) are more efficient at processing neural network algorithms. So there is a high demand for data centers who have lots of high-end GPUs.
Data center managers and operators also need to be aware of the restrictions regarding using GPUs from different manufacturers. For example, NVIDIA restricts certain graphics cards for data center use. Awareness of neural network related processing issues can help data center managers and operators better prepare their facilities to serve AI applications.
Data Center Optimization
Artificial neural networks can be an invaluable resource for data centers. Suppose, you are responsible for a Los Angeles colocation. If you have multiple clients, it’s hard to predict workloads. But a neural network based application can learn the workload behaviors of different clients and help you better manage your resources.
Neural network applications are good at detecting abnormalities in servers or networks. So these applications can help data centers improve cybersecurity algorithms and better protect against threats.
Automation of Future Data Centers
Neural network applications can also help data centers with automation. Future data centers can be equipped with robots that can perform repair and maintenance. Neural networks can help the robots learn about the servers and network equipment. It can be a cost-effective way to increase data center efficiency.
The potential for neural network-based applications is endless. So there will be more and more companies adopting neural networks to process text, voice, image, and videos. Data centers need to be prepared to serve these customers. At the same time, data centers can also use neural networks to improve their own operational efficiency.