Redundant Computation

Redundant computation, the unnecessary repetition of calculations within computational systems, is a significant challenge across diverse fields, from deep learning to multi-agent systems. Current research focuses on identifying and mitigating this redundancy through techniques like approximating similar computational blocks in neural networks, employing locally centralized execution in multi-agent settings, and strategically introducing redundancy to enhance the resilience of large model training on preemptible cloud resources. Addressing redundant computation is crucial for improving efficiency, reducing costs, and enhancing the reliability and scalability of various AI systems and algorithms.