Computational Cost

Computational cost, the resources (time, energy, memory) required for computation, is a critical concern across numerous scientific domains, driving research towards more efficient algorithms and architectures. Current efforts focus on optimizing existing models like large language models (LLMs), convolutional neural networks (CNNs), and reinforcement learning agents, often employing techniques such as parameter-efficient fine-tuning, pruning, and divide-and-conquer strategies to reduce computational demands. Addressing this challenge is crucial for enabling the wider adoption of computationally intensive methods in resource-constrained environments and accelerating scientific discovery across diverse fields, from AI and robotics to healthcare and materials science.