What Is an AI Processor?

Today’s technologists have a broad array of options for AI processors, including both traditional CPUs and AI accelerator technologies. AI processors play an essential role in any AI solution architecture because AI workloads are uniquely demanding. As a result, selecting an AI processor is a critical decision in achieving the desired speed, efficiency, and scalability for AI applications.

While AI workloads have often been seen as requiring a discrete accelerator such as a graphics processing unit (GPU) or field-programmable gate array (FPGA), modern general-purpose CPUs are showing the increasing capability to handle complex AI operations on their own in both deployment and training. Today’s CPUs—across edge, data center, cloud, and client—include integrated AI optimizations and accelerators that increase AI performance and help maximize efficiency and scalability.

That said, AI accelerators still play a critical role in addressing constantly evolving AI processing needs. This market category—a relatively recent and still-maturing evolution—includes both general-purpose devices like GPUs and FPGAs being applied to AI workloads and purpose-built AI technologies, including tensor processing units (TPUs) and neural processing units (NPUs).

Role of Processors in AI

AI processors are the core of any AI server or AI hardware system, including embedded devices. As such, the processor technologies included in a solution design are among the most important factors in its success. AI processors handle the complex computations, such as matrix multiplications, required to power AI workloads. They’re used to power AI use cases ranging from advanced analytics and prediction to computer vision, scientific simulation, generative AI (GenAI), natural language processing, and beyond.

To enable these use cases across industries, AI processors play a fundamental role in the end-to-end AI workflow, from data prep and training to deployment and continuous optimization/retraining.

In high-performance computing (HPC) contexts such as academic research or advanced analytics, large numbers of AI processors are connected via a network fabric to solve incredibly complex problems on a massive scale. They’re also deployed at the edge to handle data processing closer to the data source, often in environments constrained by power and space restrictions. AI processors are additionally used in on-premises and cloud data centers across a broad range of sizes to support end-to-end AI workloads.

From a client computing perspective, AI processors fuel end-user AI experiences on the PCs they use every day. Here, AI is playing an increasing role in augmenting the user’s daily work experience and improving their productivity. The processors used in these end-user devices need to be capable of delivering on the new requirements for AI when the workload is running locally.

Benefits of AI Processors

AI processors deliver significantly enhanced AI performance compared to general-purpose processors that aren’t equipped for AI. The specific benefits of AI processors vary between the different technology types.

For example, by opting to use an AI-optimized CPU offering as a stand-alone AI processor for lower-complexity tasks, you can often achieve the desired performance without introducing additional hardware. By choosing GPUs or purpose-built AI accelerators—deployed in a parallel computing model with a CPU—you can supercharge AI performance to meet the demands of high-complexity workloads. FPGAs may make for ideal AI processor options in edge areas where efficiency and flexibility are essential or when looking to offload specific functions from the CPU. Identifying the right processor for your workload requirements is essential to maximizing their benefits.

Overall, the core benefits of selecting the right AI processor, or combination of processors, for your objective will encompass areas like performance, scalability, cost-effectiveness, and energy efficiency. You’ll also want to assess options based on factors like adoption from a development perspective, market longevity for areas where stable designs are important, and features such as I/O support and connectivity options that can play a crucial role in AI use cases.

AI Processor Solutions

To help you select the right AI processor solution for your technology and business needs, some important questions to ask include:

• How many parameters will my AI processor need to handle during data prep and training?
• What kind of latency and throughput requirements do I need for inferencing/deployment?
• What kind of power, space, and environmental requirements do I have to account for at the edge?
• What kind of scale am I looking to achieve?

CPUs—especially those with integrated AI capabilities—are a great option for those who want to tap into the power of AI while taking a lean and efficient approach. Workloads with high levels of complexity will introduce the need for additional hardware, but many simpler AI tasks can be performed on stand-alone CPU architectures that feature integrated AI accelerators.

When properly selected, processors help solve a number of business and technology problems related to AI, including lowering TCO to help AI innovations deliver a desirable return on investment.

To summarize, the overall landscape of AI processor solutions that you can use to power your AI initiatives includes:

• CPUs when taking advantage of integrated AI acceleration and optimization.
• Discrete AI accelerators, including GPUs and FPGAs.
• Purpose-built AI accelerators, including NPUs and TPUs.

To get inspiration for how AI could impact your organization, you can explore AI use cases by industry.