Researchers are exploring photonic computing as an alternative to silicon-based technologies due to the difficulties in manufacturing tiny silicon transistors.
The never-ending quest for faster, smaller computers that can do more has led manufacturers to design ever tinier transistors that are now packed into computer chips by the tens of billions.
And so far, this tactic has worked. Computers have never been more powerful than they are now. But there are limits: Traditional silicon transistors can only get so small because of difficulties in manufacturing devices that are, in some cases, only a few dozen atoms wide. In response, researchers have begun developing computing technologies, like quantum computers, that do not rely on silicon transistors.
Another avenue of research is photonic computing, which uses light in place of electricity, similar to how fiber optic cables have replaced copper wires in computer networks. New research by Caltech’s Alireza Marandi, assistant professor of electrical engineering and applied physics, uses optical hardware to realize cellular automata, a type of computer model consisting of a “world” (a gridded area) containing “cells” (each square of the grid) that can live, die, reproduce, and evolve into multicellular creatures with their own unique behaviors. These automata have been used to perform computing tasks and, according to Marandi, they are ideally suited to photonic technologies.
“If you compare an optical fiber with a copper cable, you can transfer information much faster with an optical fiber,” Marandi says. “The big question is can we utilize that information capacity of light for computing as opposed to just communication? To address this question, we are particularly interested in thinking about unconventional computing hardware architectures that are a better fit for photonics than digital electronics.”
Cellular automata
To fully grasp the hardware Marandi’s group designed, it is important to understand what cellular automata are and how they work. Technically speaking, they are computational models, but that term does little to help most people understand them. It is more helpful to think of them as simulated cells that follow a very basic set of rules (each type of automata has its own set of rules). From these simple rules can emerge incredibly complex behaviors. One of the best-known cellular automata, called The Game of Life or Conway’s Game of Life, was developed by English mathematician John Conway in 1970. It has just four rules that are applied to a grid of “cells” that can either be alive or dead. Those rules are:
- Any live cell with fewer than two live neighbors dies, as if by underpopulation.
- Any live cell with more than three live neighbors dies, as if by overcrowding.
- Any live cell with two or three live neighbors lives to the next generation.
- Any dead cell with exactly three live neighbors will come to life, as if by reproduction.
A computer running the Game of Life repeatedly applies these rules to the world in which the cells live at a regular interval, with each interval being considered a generation. Within a few generations, those simple rules lead to the cells organizing themselves into complex forms with evocative names like loaf, beehive, toad, and heavyweight spaceship.
