This rendering shows a novel piece of hardware, called a smart transceiver, that uses technology known as silicon photonics to dramatically accelerate one of the most memory-intensive steps of running a machine-learning model. This can enable an edge device, like a smart home speaker, to perform computations with more than a hundred-fold improvement in energy efficiency. Credits : Image: Alex Sludds. Edited by MIT News.
This rendering shows a novel piece of hardware, called a smart transceiver, that uses technology known as silicon photonics to dramatically accelerate one of the most memory-intensive steps of running a machine-learning model. This can enable an edge device, like a smart home speaker, to perform computations with more than a hundred-fold improvement in energy efficiency. Credits : Image: Alex Sludds. Edited by MIT News. A new method uses optics to accelerate machine-learning computations on smart speakers and other low-power connected devices. Ask a smart home device for the weather forecast, and it takes several seconds for the device to respond. One reason this latency occurs is because connected devices don't have enough memory or power to store and run the enormous machine-learning models needed for the device to understand what a user is asking of it.
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