Accuracy Trade

Accuracy trade, in the context of machine learning and signal processing, explores the inherent tension between achieving high accuracy and minimizing resource consumption (e.g., energy, computation, memory). Current research focuses on optimizing this trade-off across diverse applications, employing techniques like approximate computing, model compression (e.g., ternary neural networks, binary neural networks), and ensemble methods to improve efficiency without significant accuracy loss. These advancements are crucial for deploying resource-constrained applications, such as those in edge computing, IoT devices, and wearable sensors, while also improving the efficiency and fairness of larger-scale machine learning models.