Cocoon is a research project that uses artificial intelligence to automatically optimize other AI systems. Instead of relying on human experts to manually fine-tune code and models, Cocoon combines evolutionary algorithms and AI techniques to make systems such as large language models run faster and more efficiently. This reduces computing time, energy use and operational costs.

Problem

Modern AI models, particularly large language models, require massive computational resources to train and deploy. Optimizing their code and execution is difficult, time-consuming, and typically performed manually by professionals. This reduces performance gains while increasing expenses, energy consumption, and development time.

Solution

Cocoon uses an evolutionary optimization process in which AI systems optimize themselves by automatically exploring, evaluating and refining alternative implementations of code and model components. Starting from a defined optimization goal, the system generates candidate solutions, tests them using a performance evaluation framework, and iteratively improves them based on measured results. This process enables AI models and their deployment pipelines to become faster and more efficient without manual tuning, making advanced AI more scalable and cost-effective.

Success Story

• Industry: AI
• Results: Cocoon-based techniques achieved a 30% improvement in runtime performance in AI workloads.
• Impact: These performance improvements reduced the computing resources required to run large AI systems, translating into millions in cost savings and lower energy consumption.