Description: Inference latency refers to the time delay that occurs between the input of data into an artificial intelligence system and the output of results after processing that data. This concept is crucial in applications where response speed is essential, such as in voice recognition systems, computer vision, and natural language processing. Inference latency is measured in milliseconds and can be influenced by various factors, including the complexity of the artificial intelligence model, the hardware capabilities used, and software optimization. In the context of edge inference, where computations are performed on local devices rather than remote servers, inference latency becomes even more critical, as users expect quick, real-time responses. Reducing this latency is a constant goal in the development of artificial intelligence technologies, as it directly impacts user experience and application effectiveness. Therefore, understanding and optimizing inference latency is essential for the success of artificial intelligence-based solutions, especially in environments where immediacy is key.
