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Related Concept Videos

Ampere-Maxwell's Law: Problem-Solving01:17

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
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Reconfigurable photonic neural networks: distribution-aligned calibration and dynamic resource allocation for

Songcheng Zhou, Ziqiang He, Yiheng Zhao

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    |November 11, 2025
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    Summary

    We developed dynamic configuration algorithms for photonic computing to enhance AI performance. These methods significantly improve inference accuracy and reduce energy consumption for large-scale AI workloads.

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    Area of Science:

    • Photonics
    • Artificial Intelligence
    • Computer Engineering

    Background:

    • The increasing demand for generative and large language models drives up computational needs and energy costs in data centers.
    • Photonic computing offers a solution with high bandwidth, low energy use, and low latency for AI.
    • Current photonic AI research often uses fixed parameters, limiting system efficiency and performance.

    Purpose of the Study:

    • To address the limitations of fixed parameter settings in photonic AI systems.
    • To enhance energy efficiency and compute density in photonic computing for AI.
    • To introduce dynamic configuration schemes for optical computing accelerators.

    Main Methods:

    • Proposed a distribution-alignment calibration (DAC) algorithm for photonic convolution.
    • Implemented dynamic power allocation (DPA) and dynamic dimension allocation (DDA) schemes.
    • Validated the algorithms using a calibrated photonic-computing simulator and hardware experiments.

    Main Results:

    • DAC improved inference accuracy from 12.93% to 75.77% at 5 dBm input power.
    • DDA reduced power consumption by up to 20.9% for ResNet models.
    • DPA increased compute density by up to 28.19% and achieved significant optical power savings.

    Conclusions:

    • Dynamic adjustment of optical power and computational scale optimizes photonic computing systems.
    • The proposed DAC, DPA, and DDA schemes enable more flexible, energy-efficient, and high-performance AI.
    • Photonic computing with dynamic configurations is a viable path for future AI infrastructure.