ARIA Opportunity Space: Nature Computes Better

Leverage artificial intelligence to engineer ultra-efficient and ultra-fast physics-based neuromorphic computing architectures inspired by biological systems. Utilizing nanostructured lasers integrated on silicon chips, this project aims to dramatically enhance speed and efficiency of AI inference, and exploit unique strengths of bio-inspired hardware including strong few-show learning. By employing novel AI training methods such as reinforcement learning and gradient-free training, the research will develop techniques to harness laser-based computational layers that operate at femtosecond speeds with nanojoule energy consumption. The project will demonstrate practical applications on benchmark datasets like CIFAR-100 and will innovate on few-shot learning algorithms, paving the way for transformative efficiency gains in robotics, diagnostics, and next-generation computing.