FAQs About Using AI Chips in Your Business

The last few years have seen an increase in Artificial Intelligence (AI) in businesses. Since 2010, these machines have been improved until their computing capacities were substantial enough to handle deep learning in neural networks. This has made AI hardware crucial for machine learning processes. 

With the increased number of businesses relying on AI, more businesses have improved the chips necessary for these machines to run. Currently, businesses have the option to rent this hardware from cloud-based services. Otherwise, they can purchase their own hardware, which is a great idea if they plan to use it a lot. However, before investing in either option, you should learn more about the chips that make them work and the process for incorporating them into your business. 

What Are AI Chips?

AI chips, also known as neural network chips, are specially designed to accelerate the processes in neural networks. For the most part, this means that they can help a machine undertake deep learning. Hence, neural networks are one of the subcategories of AI.

Basically, it is machine learning that mimics the human brain. The neurons in the machines are mathematical functions designed to mimic how human neurons work. These networks can be deep and consist of multiple layers. Machine learning that uses these deep networks is called deep learning. These are generally used in commercial settings for training and interference. 

What Are Their Components?

An AI chip consists of three primary hardware components, the networking, storage, and computing portions. While this hardware's processing and computing parts have developed rapidly, the storage and networking parts of AI chips are still in the works. That said, several major tech companies are currently striving to improve on these capacities. 

Why Are They Higher Performing Than Regular Chips?

Regular chips use basic arithmetic blocks to perform in-memory calculations. However, because of the serial processing methods, they are not capable of deep learning, take longer to perform multiple calculations, perform calculations individually, and require many expensive chips. 

Why Are They Important Now?

Today, most commercial AI solutions require deep learning provided by neural networks. The number and use of these applications have been growing steadily since they were first developed and are only predicted to continue growing. Additionally, they are essential because they can help businesses increase the amount of profit they draw in. 

What Are Their Designs?

There are several different types of designs that these chips use to gain their improved performance. The various designs used include GPUs, wafer chips, reconfigurable neural processing units, neuromorphic chip architectures, and analog memory-based technology. 

What Criteria Should You Use To Assess This Hardware?

Obviously, the needs of your team and company should be the first thing you think about. For example, if your team isn't going to be using AI chips often enough to justify purchasing them, you should try cloud-based solutions and run your software on several GPUs. However, using this setup too often can be more costly than purchasing on-site hardware. Plus, you may start by testing the AI waters with cloud-based software to see if your team adjusts well before investing in on-site hardware. 

Are There Hardware Benchmarks?

While you would probably like to see a performance benchmark before purchasing AI chips, these are difficult to obtain for deep learning programs. Therefore, you will need to compare your AI chip options in terms of power consumption and speed to figure out which one will be better for you. 

Who Are the Leading Providers of This Hardware?

AI chips are very complex, which has eliminated startups and small businesses from producing these. Therefore, the major tech companies are also the leading AI hardware developers. When you find out that Intel required three years before it could produce its first AI chip, you'll understand why. 

There are plenty of options when it comes to placing AI in your business. Now that you know more about the chips that make them work quickly, you can take the next steps to implement these devices into your company processes.