Parallel in-memory photonic computing using continuous-time data representation

Applications: Data communication, photonic computing, optical neural networks, machine learning, deep learning, edge computing

The parallelism enhancement from each of ‘continuous-time data representation’ (N) and ‘in-memory photonic computing’ (M/p) can be synergized to reach a higher MVM computation parallelism (N×M/p)

Features	Benefits
Light is the data carrier rather than electric signals	Far more information can be transmitted in a given time frame – a THz data rate.
Continuous-time data representation	Using this instead if binary data representation allows for full benefits of the optical bandwidth to enhance data communication parallelism. It has been shown to embed 50 values in one time cycle.
Both continuous-time data representation methods from electronics and the photonic wavelength-division multiplexing are combined	This achieves a significantly higher number of simultaneous data streams
Large frequency difference between discrete optical frequencies	So each optical frequency can act as a carrier frequency without interference.

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Parallel in-memory photonic computing using continuous-time data representation

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