Florida team builds chip to run AI tasks 100-fold at lower power cost

Researchers at the University of Florida have developed a novel silicon photonic chip that uses light, rather than solely electricity, to perform convolution operations—key computations in AI tasks such as image and pattern recognition. By integrating optical components like laser light and microscopic Fresnel lenses directly onto the chip, the device can execute these operations much faster and with significantly lower energy consumption. Tests demonstrated that the prototype achieved about 98% accuracy in classifying handwritten digits, comparable to conventional electronic chips, while operating at near-zero energy for these computations. A notable innovation of this chip is its ability to process multiple data streams simultaneously through wavelength multiplexing, using lasers of different colors passing through the lenses concurrently. This parallel processing capability enhances efficiency and throughput. The project, involving collaboration with UCLA and George Washington University, aligns with trends in the industry where companies like NVIDIA are already incorporating optical components into AI hardware. The researchers anticipate that chip-based optical computing will become integral to future AI systems, potentially enabling more sustainable scaling of AI technologies