Researchers at MIT have developed a brand-new technique for performing computations straight on low-power devices that significantly lowers this latency. Their method moves the memory-intensive machine-learning model operations to a central server where the model's parts are encoded onto light waves.
Fiber optics is used to transfer the waves to a connected device, allowing massive amounts of data to be sent via a network at incredible speeds. The receiver then makes use of a simple optical device to quickly compute using the components of a model delivered by those light waves. When compared to previous ways, this technology increases energy efficiency by more than a hundred times. Since a user's data won't need to be sent to a central site for processing, it might also increase security.
In machine learning, neural networks use layers of interconnected nodes, or neurons, to identify patterns in datasets and carry out tasks like speech recognition and image classification. The weight parameters in these models, however, which are numerical values that alter the input data as it is processed, can number in the billions. These weights need to be remembered. At the same time, billions of algebraic computations are needed to complete the data transformation process, which consumes a lot of power.
They created the Netcast neural network architecture, which stores weights in a central server coupled to a revolutionary piece of hardware known as a smart transceiver. The silicon photonics technology used by this smart transceiver, a thumb-sized data receiver and transmitter, allows it to retrieve trillions of weights from memory per second. Weights are received as electrical signals, which are then imprinted onto light waves. The transceiver transforms the weight data, which are encoded as bits (1s and 0s), by turning on and off lasers. A laser is turned on for a 1 and off for a 0. In order to avoid having a client device contact the server in order to get them, it mixes these light waves and then periodically sends them across a fiber optic network.
The broadband "Mach-Zehnder" modulator, a straightforward optical component, exploits the light waves once they reach the client device to carry out extremely quick analog computing. This entails writing input data from the apparatus—like sensor data—onto the weights. The receiver then receives each particular wavelength and detects the light, measuring the computation's outcome. The researchers came up with a method to leverage this modulator to perform billions of multiplications per second, greatly speeding up processing on the device while consuming very little power.
In order to improve performance even
further, the researchers want to iterate on the smart transceiver chip in the
future. In order for the receiver, which is presently the size of a shoe box,
to fit on a smart device like a cell phone, they also want to reduce its size
to that of a single chip.
- Delocalized photonic deep learning on the internet’s edge | Science
- Deep learning with light | MIT News | Massachusetts Institute of Technology
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