TL:DR: A GPU (Graphics Processing Unit) is a specialized processor designed to handle complex graphics and parallel processing tasks, enhancing performance in gaming, visual computing, and machine learning.
A GPU (Graphics Processing Unit) is a specialized processor primarily designed to accelerate graphics rendering by handling multiple tasks simultaneously. Originally built for rendering images and video in gaming and visual applications, GPUs are now also widely used in scientific computing, artificial intelligence, and machine learning due to their ability to process large amounts of data in parallel.
In video surveillance, GPUs play a key role in accelerating the processing and analysis of high-resolution video streams. They enable faster video encoding, real-time analytics, and advanced features like facial recognition, weapon detection, and object tracking by efficiently handling large volumes of video data. This makes GPUs essential for surveillance systems that require quick, accurate analysis, especially in AI-driven setups where machine learning algorithms are used to detect and respond to events in real-time.
A GPU works by utilizing many smaller cores designed for handling multiple operations at once. Unlike a CPU (Central Processing Unit), which has fewer cores optimized for sequential processing, a GPU’s structure is built for parallel processing. This setup allows it to perform numerous calculations simultaneously, making it effective for tasks that involve large amounts of data processed in parallel, such as rendering graphics and running machine learning algorithms.
In video surveillance, this parallel structure is useful. A GPU processes video frames at the same time, accelerating tasks like object detection, motion analysis, and anomaly detection in real-time. Through frameworks such as CUDA (Compute Unified Device Architecture) from NVIDIA, developers can write applications that harness this power for specialized uses like deep learning.