Operator Level Optimization

Operator-level optimization focuses on improving the efficiency of executing computational tasks, particularly within complex systems like deep neural networks and multi-robot teams. Current research emphasizes automated methods for optimizing operator scheduling and placement, employing techniques such as constrained optimization, reinforcement learning, and learned cost models to achieve significant performance gains. These advancements are crucial for accelerating deep learning inference and training, enhancing the performance of distributed stream processing systems, and improving human-robot collaboration in complex scenarios.