Cross Layer

Cross-layer optimization aims to improve system performance by coordinating design and control across different layers of a system's architecture, such as application, transport, network, and physical layers. Current research focuses on applying this principle to diverse areas, including large language model compression (using techniques like singular value decomposition and parameter sharing), efficient deep learning acceleration (through novel scheduling algorithms and hardware-software co-design), and resource allocation in wireless networks (employing deep reinforcement learning and adaptive mechanisms). These advancements hold significant promise for enhancing efficiency, performance, and reliability in various applications, from AI and machine learning to communication systems and smart grids.