Nanophotonic Analog Processor For High Performance Computing
Researchers at George Washington University have developed an all-optical analog accelerator to solve partial differential equations.
Power-intensive computational tasks are highly complex and therefore require a significant amount of energy. This is due to the digital computing architectures realized today. These implementations do not scale well with the problem complexity. Therefore, analog accelerators can be used to take the load off from traditional computers by solving specific complex processes.
Researchers from George Washington University suggest that analog photonic solutions offer unique opportunities to address complex computational tasks with unprecedented performance in terms of energy dissipation and speeds, overcoming current limitations of modern computing architectures based on electron flows and digital approaches. However, the one thing that has throttled back the development of an all-optical analog computing platform is the lack of modularization and lumped element reconfigurability in photonics.
In the research published in Communications Physics, researchers explore, using numerical simulation, a nanophotonic platform based on epsilon-near-zero materials capable of solving in the analog domain partial differential equations.
The nano-optical analog processor developed by the researchers can be integrated at chip-scale, processing arbitrary inputs at the speed of light.