Fully reconfigurable coherent optical vector–matrix multiplication

Полностью перенастраиваемое когерентное оптическое умножение вектор–матрица
James Spall, Xianxin Guo, Thomas D. Barrett, A. I. Lvovsky
2020-09-14

coherent optical vector–matrix multiplicationoptical neural networksparallel vector-vector multiplicationreconfigurable free-space optical multiplierspatial light modulators
Optics is a promising platform in which to help realize the next generation of fast, parallel, and energy-efficient computation. We demonstrate a reconfigurable free-space optical multiplier that is capable of over 3000 computations in parallel, using spatial light modulators with a pixel resolution of only 340×340. This enables vector-matrix multiplication and parallel vector-vector multiplication with vector size of up to 56. Our design is, to the best of our knowledge, the first to simultaneously support optical implementation of reconfigurable, large-sized, and real-valued linear algebraic operations. Such an optical multiplier can serve as a building block of special-purpose optical processors such as optical neural networks and optical Ising machines.
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Demonstrated a reconfigurable free-space optical multiplier capable of performing over 3000 computations in parallel
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Enabled vector-matrix multiplication and parallel vector-vector multiplication with vector size up to 56
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First reported design to simultaneously support optical implementation of reconfigurable, large-sized, real-valued linear algebraic operations
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Proposed optical multiplier as a building block for special-purpose optical processors (optical neural networks and optical Ising machines)
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Used spatial light modulators with pixel resolution 340×340 to enable the system

Reconfigurable free-space coherent optical multiplier using spatial light modulators (340×340 pixels) for vector–matrix and parallel vector–vector multiplication

Optical implementation and performance of reconfigurable, large-sized, real-valued linear algebra operations (vector–matrix and parallel vector–vector multiplication) with up to 3000 parallel computations and vector size up to 56

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2020-09-14
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Authors
James Spall
Xianxin Guo
Thomas D. Barrett
A. I. Lvovsky
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