Why AI inference is happening on the CPU, the different technological approaches for AI inference, and examples of AI inference use cases, from the cloud to the edge.
As AI continues to revolutionize industries, new workloads, like generative AI, inspire new use cases, the demand for efficient and scalable AI-based solutions has never been greater. While training often garners attention, inference—the process of applying trained models to new data—is essential for AI workloads, whether they are running in the cloud, or enabling real-world applications at the edge — on devices.
Inference covers the most widely used AI and machine learning (ML) workloads and use cases. On consumer devices, common AI workloads like object detection and facial recognition, as well as text generation and summarization, are all inference. In cars, AI inference workloads are used for autonomous and assisted-driving capabilities, while in IoT, inference is supporting the move to advanced automation. Inference is everywhere. In general, AI is best managed through heterogeneous compute approaches that give technology companies the flexibility to use different compute components, including the CPU, GPU, and NPU, for different AI use cases and demands. However, there are many cases where the CPU is optimal for processing AI workloads, with these requiring the levels of performance, efficiency, and security that the CPU provides. In fact, many AI inference workloads can run on the CPU, the easiest target for developers when creating their own AI-based applications. This is largely due to its ubiquity, ease of programmability, and general-purpose flexibility, as well as the latency and memory locality advantages when compared to other processors.
Read more here.
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
Leave a Reply