A photonic processor uses light signals and a residue number system (RNS) to perform calculations. The processor sums two or more values by shifting the phase of a light signal with phase shifters and reading out the summed phase with a coherent detector. Because phase winds back every 2π radians, the photonic processor performs addition modulo 2π. A photonic processor may use the summation of phases to perform dot products and correct erroneous residues. A photonic processor may use the RNS in combination with a positional number system (PNS) to extend the numerical range of the photonic processor, which may be used to accelerate homomorphic encryption (HE)-based deep learning.

An integrated photonics computing system implements a residue number system (RNS) to achieve orders of magnitude improvements in computational speed per watt over the current state-of-the-art. RNS and nanophotonics have a natural affinity where most operations can be achieved as spatial routing using electrically controlled directional coupler switches, thereby giving rise to an innovative processing-in-network (PIN) paradigm. The system provides a path for attojoule-per-bit efficient and fast electro-optic switching devices, and uses them to develop optical compute engines based on residue arithmetic leading to multi-purpose nanophotonic computing.

An integrated photonics computing system implements a residue number system (RNS) to achieve orders of magnitude improvements in computational speed per watt over the current state-of-the-art. RNS and nanophotonics have a natural affinity where most operations can be achieved as spatial routing using electrically controlled directional coupler switches, thereby giving rise to an innovative processing-in-network (PIN) paradigm. The system provides a path for attojoule-per-bit efficient and fast electro-optic switching devices, and uses them to develop optical compute engines based on residue arithmetic leading to multi-purpose nanophotonic computing.

A photonic processor uses light signals and a residue number system (RNS) to perform calculations. The processor sums two or more values by shifting the phase of a light signal with phase shifters and reading out the summed phase with a coherent detector. Because phase winds back every 2? radians, the photonic processor performs addition modulo 2?. A photonic processor may use the summation of phases to perform dot products and correct erroneous residues. A photonic processor may use the RNS in combination with a positional number system (PNS) to extend the numerical range of the photonic processor, which may be used to accelerate homomorphic encryption (HE)-based deep learning.