In fact, CPU is the most suitable for AI reasoning? For these five reasons

Advanced artificial intelligence (AI), such as generative AI, is augmenting all of our smart devices. However, a common misconception is that these AI workloads can only be handled in the cloud and data centers. In fact, most AI inference workloads can be handled at the edge (i.e. on the actual device), and these workloads are cheaper to run and faster than training.

Today, the adoption of cpus on a variety of devices and growing AI capabilities are helping to push more AI inference processing to the edge. While heterogeneous computing approaches give the industry the flexibility to use different computing components, including cpus, Gpus, and Npus, to meet different AI use cases and needs, AI reasoning in edge computing is where cpus shine.

With this in mind, here are five reasons why the CPU is the best target for AI inference workloads.

Benefits of AI inference on the CPU

Edge efficiency

Edge AI processing is important to the tech industry because the more edge AI processes, the less data needs to be sent to or from the cloud for processing, and the more power can be saved. This will result in significant energy and cost savings, and since the data is processed locally, users can also enjoy a faster and more responsive AI inference experience as well as greater privacy. These are especially important for power-constrained devices and edge applications, such as drones, smart wearables and smart home devices, where power consumption, latency and security are critical. In this case, the CPU plays a key role because it is able to handle these AI inference tasks in the most efficient way.

Suitable for a variety of AI inference tasks

The versatility of the CPU enables it to handle a variety of AI inference tasks, especially for applications and devices that require fast response and reliable performance. For example, real-time data processing tasks such as predictive maintenance, environmental monitoring, or autonomous navigation can be handled more efficiently and quickly on the CPU. In industrial iot applications, this ensures that the system can respond to its environment or any changes in its environment within milliseconds, which is critical for safety and functionality.

Excellent performance for small AI models

The CPU supports various AI frameworks, such as Meta's PyTorch and ExecuTorch and Google AI Edge's MediaPipe, so it is easy to deploy large language models (LLMS) for AI inference. These LLMS are evolving rapidly, and small, compact models with a decreasing number of parameters will lead to extraordinary user experiences. The smaller the model, the more efficiently it will run on the CPU.

The introduction of small LLMS, such as the new Llama 3.1b and 3B, will be critical to enabling large-scale AI reasoning. Recently, Arm demonstrated that running Llama 3.3B LLM on ARM-powered mobile devices with an Arm CPU optimized kernel can increase prompt word processing speed by up to 5 times and token generation speed by up to 3 times.

We are already seeing developers writing more compact models to run on low-power processors and even microcontrollers, saving time and money. Plumerai offers software solutions for accelerating neural networks on Arm Cortex-A and Cortex-M systems on Chip (SOCs) with just 1MB of AI code running on ARM-based microcontrollers to perform face detection and recognition. To protect user privacy, all reasoning is done on the chip, so no facial features or other personal data is sent to the cloud for analysis.

Greater flexibility and programmability for developers

Due to the flexibility and programmability of the CPU, the software community is actively choosing it as the preferred path for handling AI workloads. The greater flexibility of the CPU means that developers can run a wider range of software with more diverse data formats without the need for developers to build multiple versions of code. At the same time, new models with different architectures and quantification schemes appear every month, and these new models can be deployed to the CPU in a matter of hours due to the CPU's high programmability.

The architectural foundation of artificial intelligence innovation

This developer innovation builds on a CPU architecture that constantly adds new capabilities and instructions to handle more advanced AI workloads. The proliferation of cpus means developers have access to these capabilities, further accelerating and innovating AI-based experiences. In fact, the continuous evolution of CPU architectures is directly related to the development of faster and smarter applications today.

Why can't AI reason without CPU

The CPU is not just an integral part of SoC design, it also makes AI practical, efficient, and easy to use in a variety of edge applications and devices. Cpus combine efficiency, versatility, and accessibility, making them indispensable for AI reasoning. By processing AI tasks at the edge, cpus help reduce energy consumption and latency while providing a faster and more responsive AI experience for end users. As AI continues to evolve and infiltrate every aspect of technology, the role of cpus in handling AI inference workloads will only grow, ensuring that AI can be widely and sustainably deployed across a wide range of industries.