Latency Accuracy

Latency-accuracy trade-offs are a central challenge in many machine learning applications, particularly those with real-time constraints. Current research focuses on optimizing this trade-off through techniques like efficient scheduling algorithms (e.g., for large language model inference), adaptive inference strategies (e.g., early-exit mechanisms and dynamic model selection), and model compression methods (e.g., mixed-precision quantization and network linearization). These advancements aim to improve the efficiency and responsiveness of machine learning systems across diverse domains, from robotics and industrial IoT to cloud-based services. The ultimate goal is to enable the deployment of increasingly complex models without sacrificing performance or reliability.