HoloNeuroNet: A nano-holography-based AI optical computing framework
Anupa Sinha, Pooja Sharma · 2025
Nano-holography provides an eminent way for AI-integrated optical computing with ultrafast, energy efficiency, and scalability in next-generation artificial intelligence technologies. HoloNeuroNet, the world’s first framework that enables efficient dynamic holographic computation with AI via nano-engineered metasurface technology, has been introduced. Different from conventional electronic AI accelerators, this platform encodes, parallel processes, and optically calculates tensor using holographic (HNNs) network. The Holographic Optical Tensor Processing (HOT-P) mechanism proposed reduces latency and power consumption owing to AI operations being executed in the optical domain in place of the electronic domain. Apart from that, a quantum-nano holography approach combines quantum and nano computing to achieve high computational accuracy and robustness for ultra-fast deep learning inference. Adaptive Holographic memory in the system provides for dynamic data storage and retrieval using real-time workload demands and minimizes performance tradeoff for different AI tasks. The experimental evaluation shows a 1000x increase in the processing speed, better energy efficiency, and better precision in the performance of AI models over the conventional architectures based on semiconductors. With the demonstration of high-performance computing using nano holography, this research also shows the ability of nano holography to solve AI computing needs of edge devices, cybersecurity, healthcare, and quantum AI applications. This work opens new groundfalls to a novel paradigm of optical computing and envisages the future of AI-augmented nano-holographic processors superior to the existing machine and deep learning accelerations.