In 1958 and 1959, breakthrough innovations were developed that made the creation of the first integrated computer chip possible. Before this breakthrough, computational devices were reaching their peak usefulness due to the excessive inefficiency and maintenance difficulties of their design. Thanks to the advent of the computer chip, processing power has continued to evolve to the point where we are now using increasingly complex applications in artificial intelligence and machine learning.

However, these new applications have again pushed computer hardware to evolve. Traditional CPUs have improved at sequential processing and are perfect for most consumers. But AI experts and machine learning engineers need more than these processors can provide. Their parallel processing needs have forced them to turn to GPU processing and purpose-built field-programmable gate arrays (FPGAs).

These examples show that hardware choices are directly linked to a business’s ability to run and scale AI applications. What should organizations consider when choosing their hardware, especially the processing chips?

Chips Designed for AI Perform Better and Help Businesses Save Money

Close up of a modern computer chip

AI and machine learning have requirements that differ from other use cases like cloud computing and data storage. For one thing, they require CPUs that are customized to AI workloads. These chips perform better when designed to make many calculations in parallel, are built with AI-focused programming languages, and can store complete algorithms on a single chip.

However, even when considering processors made for AI applications, it’s still important to take into account your use case. One type of processor that performs better than CPUs for multidimensional data processing is the GPU. These processors leverage thousands of cores to handle heavy computing tasks simultaneously. They are especially powerful when you factor in their dedicated RAM, and they’re ideal for training your AI programs. 

There’s another type of processing that doesn’t use a physical chip. We’re referring to field-programmable gate arrays. Unlike traditional processors, these semiconductor integrated circuits are fully programmable by the user and can be programmed to specific functions. This makes them very efficient for simultaneous processing of the same programs but not as agile as the GPU when changes to the workload are likely. FPGAs are great for inferencing once your AI is fully trained since they can carry out repetitive, parallel tasks with great efficiency.

Choosing the right hardware design for your AI needs enables processors to perform similar tasks at better speeds and with lower energy costs. This is because you’re using tools that have been customized for the job.

Consider How All Your Hardware Will Work Together

AI computing is expensive. Like our example of early computing chips, AI is only worth the effort when the reward to businesses outweighs the cost. Companies must therefore find ways to make hardware more efficient. Aside from choosing processing hardware that matches your needs, you need to ensure that all of your hardware is optimized to work together.

An example would be to consider your network latency limits. If you’re processing power, RAM, and system bus capabilities far exceed your network bandwidth, your speed will still be hampered by that network limit. When working at scale, the costs of overspending on more powerful components than you need adds up quickly, especially if you’re not seeing the benefits to performance.

At Intequus, we help organizations build well-balanced machines for their AI applications. This balance allows businesses to finely control the total cost of ownership while getting the level of performance they need for productive AI hardware. If you want to learn more about building custom AI hardware, talk to our experts today. 

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