The escalating demands of AI, particularly for large language models, require significant increases in compute power, leading to high costs and unsustainable energy consumption for companies relying on traditional GPUs and TPUs. Scaling these systems faces limitations due to real estate constraints and diminishing returns in energy efficiency.

Neurophos is developing an optical processing unit (OPU) that leverages photonic computing to achieve substantial performance gains with significantly reduced power consumption. By miniaturizing optical modulators by a factor of 10,000, Neurophos enables a 3D photonic computing architecture that overcomes the limitations of traditional electronic systems. The OPU utilizes optical systolic arrays, where light replaces electrons, eliminating latency issues and enabling clock speeds of 100 GHz or higher. This approach allows a single Neurophos processor to deliver the performance of 100 GPUs while consuming only 1% of the power.

Ultra-dense optical modulators based on metasurface technology, 8,000 times smaller than standard silicon photonics modulators

Optical systolic arrays that use light for computation, eliminating signal propagation latency

Goodman Engine architecture for purely optical vector-matrix multiplications

Folded design for compact and efficient layout, reducing latency and enhancing scalability

3D stacking on a standard CMOS chip with dense through-silicon via (TSV) array for high-bandwidth communication

Model-agnostic design that supports multiple numerical formats without retraining

The primary target audience includes organizations involved in AI model training and inference, particularly those working with large language models and requiring high compute performance with stringent power constraints.