Model Transformation

Abstracting biological luminescence into mathematical computational models.

The image captures a serene nighttime beach scene with bioluminescent waves that glow with a vivid blue hue. The sky above is dark and scattered with stars, adding a tranquil celestial backdrop. The foreground consists of smooth, dark stones that are dimly lit by the ambient light.

Optical Computing

Testing performance of biologically-inspired computational models.

A dark, nighttime beach scene with waves glowing with bioluminescence along the shoreline under a starry sky.

A glowing jellyfish appears against a dark backdrop. The creature is transparent with luminescent blue hues, showcasing intricate patterns and fluid movements. Its body is delicately highlighted by the neon light, creating a mesmerizing and surreal effect.

Brightly colored lines of purple and blue light create an abstract pattern, with scattered letters or symbols appearing intermittently throughout the image. The effect resembles light trails or fiber optic cables in motion.

Simulation Validation

Developing environments to validate optical computing simulations effectively.

Expected outcomes include:

Multiple intersecting blue luminescent lines form a geometric mesh structure on a dark background. A purple neon light strip runs horizontally across the top portion of the image, casting a glow.

1) Establishing a theoretical framework connecting marine organism bioluminescence mechanisms with optical computing principles, providing new ideas for biomimetic computing; 2) Developing a series of optical computing algorithms and architectural models inspired by biological luminescence, surpassing traditional methods in energy efficiency and adaptability; 3) Creating a database and simulation platform for marine organism bioluminescence mechanisms, supporting interdisciplinary research and education; 4) Proposing practical optical computing hardware design schemes, laying foundations for next-generation low-energy AI accelerators. These contributions will deepen our understanding of how to transform efficient information processing mechanisms in nature into artificial computing systems, particularly regarding energy efficiency. Research results may inspire more environmentally friendly AI infrastructure designs, reducing the energy burden of large model training and inference, promoting sustainable AI development. By exploring connections between biological systems and computational architectures, this research will provide fresh perspectives for AI hardware design, promoting interdisciplinary dialogue between computer science and biology. Additionally, this work will demonstrate how large language models can serve as scientific knowledge integrators, assisting innovative technology design, providing valuable case studies for AI-assisted scientific discovery.