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Researchers have developed a new approach to heavy computation, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. The technology could be used for data centers or security systems and drones.
MIT Professor Marin Soljačić says that many researchers over the years have made claims about optics-based computers, that turned out not to be practical, while a light-based neural-network system developed by this team “may be applicable for deep-learning for some applications”.
According to MIT website, traditional computer architectures are not very efficient when it comes to the kinds of calculations needed for certain important neural-network tasks. Such tasks typically involve repeated multiplications of matrices, which can be very computationally intensive in conventional CPU or GPU chips.
The research team has come up with a way of performing these operations optically instead. “This chip, once you tune it, can carry out matrix multiplication with, in principle, zero energy, almost instantly,” Soljačić says. “We’ve demonstrated the crucial building blocks but not yet the full system.”
The new programmable nanophotonic processor, which was developed in the Englund lab by the team, uses an array of waveguides that are interconnected in a way that can be modified as needed, programming that set of beams for a specific computation.
The processor guides light through a series of coupled photonic waveguides. The team’s full proposal calls for interleaved layers of devices that apply an operation called a nonlinear activation function, in analogy with the operation of neurons in the brain.
The team says it will still take a lot more effort and time to make this system useful; however, once the system is scaled up and fully functioning, it can find many user cases, such as data centers or security systems. The system could also be a boon for self-driving cars or drones, or whenever you need to do a lot of computation but you don’t have a lot of power or time.