Which hardware architecture is most suitable for real-time video processing in an AI application?

The selection of GPUs for handling video processing and analytics is ideal due to their parallel processing capabilities. GPUs are specifically designed to perform a large number of operations concurrently, making them exceptionally efficient for the type of heavy computational workloads involved in real-time video processing. This capability is crucial in AI applications that require processing large volumes of data quickly, such as analyzing frames from video streams or executing complex algorithms for object detection and recognition.

GPUs also benefit from specialized hardware features, such as high memory bandwidth and optimized architectures for matrix and tensor operations, which are commonly used in AI tasks. This leads to faster processing times compared to CPUs, which are generally optimized for sequential processing and may struggle with the same workloads when tasked with real-time high-resolution video streams.

In contrast, while a combination of CPUs and FPGAs could offer flexibility and customization for specific tasks in video processing, it would not match the raw processing power and speed that GPUs provide for real-time applications. Additionally, relying solely on CPUs for all video processing tasks would likely result in higher latency and reduced performance, as they cannot handle the massive parallel operations as efficiently as GPUs.

Overall, deploying GPUs is the most suitable solution for real-time video processing in AI applications, ensuring high-speed performance and the ability to