Artist’s rendition of a computer system based on light that could jumpstart the power of machine-learning programs like ChatGPT. Blue sections represent the micron-scale lasers key to the technology. Credit: Ella Maru Studiosystem demonstrates greater than 100-fold improvement in energy efficiency and a 25-fold improvement in compute density compared with current systems.
Further, because the components of the system can be created using fabrication processes already in use today, “we expect that it could be scaled for commercial use in a few years. For example, the laser arrays involved are widely used in cell phone face ID and data communication,” says Zaijun Chen, first author, who conducted the work while a postdoc at MIT in the Research Laboratory of Electronics and is now an assistant professor at the University of Southern California.
He continues, “We don’t know what capabilities the next-generation ChatGPT will have if it is 100 times more powerful, but that’s the regime of discovery that this kind of technology can allow.” Englund is also leader of MIT’s Quantum Photonics Laboratory and is affiliated with the RLE and the Materials Research Laboratory.The current work is the latest achievement in a drumbeat of progress over the last few years by Englund and many of the same colleagues.
However, current optical neural networks have significant challenges. For example, they use a great deal of energy because they are inefficient at converting incoming data based on electrical energy into light. Further, the components involved are bulky and take up significant space. while ONNs are quite good at linear calculations like adding, they are not great at nonlinear calculations like multiplication and “if” statements.
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