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Optimizing stochastic gradient descent algorithms for serially addressed adaptive-optics wavefront modulators.

Travis Simpkins¹, Jeremy Hui, Cardinal Warde

¹Optron Systems Inc., Bedford, MA 01730, USA. simpkins@optronsystems.com

|October 24, 2007

Summary

Serial gradient descent algorithms are faster for high-resolution adaptive optics systems using serial modulators. Despite needing more iterations, faster serial updates overcome databus limitations for quicker wavefront correction.

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Area of Science:

Optics and Photonics
Computer Science

Background:

High-resolution adaptive-optical systems require spatial light modulators (SLMs).
Serial-addressed SLMs, such as microelectromechanical-system-on-VLSI, are likely for systems with millions of pixels.

Purpose of the Study:

Compare parallel and serial gradient descent adaptive-optics algorithms.
Determine the optimal algorithm-modulator pairing for high-resolution adaptive optics.

Main Methods:

Simulated serial gradient descent on serial modulators.
Simulated parallel gradient descent on parallel modulators.
Compared convergence time, considering databus limitations.

Main Results:

Serial algorithms require more iterations than parallel algorithms.
Each serial iteration is significantly faster due to serial modulator architecture.
Serial algorithms achieve faster convergence times on serial modulators.

Conclusions:

Serial gradient descent algorithms are favored for high-resolution adaptive optics using serial-addressed SLMs.
Databus bandwidth limitations make serial updates more time-efficient overall.
Serial modulators are better matched with serial-update wavefront correction algorithms.