Presentation
7 March 2022 FemtoComputing: machine learning using femtosecond pulses and nonlinear optics
Bahram Jalali, Tingyi Zhou, Fabien Scalzo
Author Affiliations +
Abstract
We report a new concept in hardware acceleration of AI that exploits femtosecond pulses for both data acquisition and computing. Data is first modulated onto the spectrum of a supercontinuum laser. Nonlinear optical propagation then projects the data into an intermediate space in which data classification accuracy is enhanced. This nonlinear optical kernel operation improves the linear classification results similar to a traditional numerical kernel (such as the radial-basis-function) but with orders of magnitude lower latency. The performance is data-dependent due to the limited degrees of freedom in the optical part of the system.
Conference Presentation
© (2022) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bahram Jalali, Tingyi Zhou, and Fabien Scalzo "FemtoComputing: machine learning using femtosecond pulses and nonlinear optics", Proc. SPIE PC11999, Ultrafast Phenomena and Nanophotonics XXVI, PC119990D (7 March 2022); https://doi.org/10.1117/12.2613827
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KEYWORDS
Femtosecond phenomena

Machine learning

Nonlinear optics

Artificial intelligence

Time metrology

Space operations

Spectroscopy

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