Hybrid training of optical neural networks

Spall, James; Guo, Xianxin; Lvovsky, A. I.

doi:10.1364/fio.2022.ftu6d.2

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Several algorithms have been proposed to train such network architectures, which can be summarized as the following optimization question (41,42) where ϕ are angles of diagonal elements in the Φ. x t and v t are the input data and response data in the training dataset, where the optimization process seeks for the optimum phase distribution inside the network so that the difference between ground truth v t and the network outputs f pla−NN (x t ; Φ; W) reaches its minimum. The loss function f loss defines such a difference or distance.…”

Section: Principlesmentioning

confidence: 99%

Direct electromagnetic information processing with planar diffractive neural network

Gu,

Ma,

Gao

et al. 2024

Sci. Adv.

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Diffractive neural network in electromagnetic wave–driven system has attracted great attention due to its ultrahigh parallel computing capability and energy efficiency. However, recent neural networks based on the diffractive framework still face the bottlenecks of misalignment and relatively large size limiting their further applications. Here, we propose a planar diffractive neural network (pla-NN) with a highly integrated and conformal architecture to achieve direct signal processing in the microwave frequency. On the basis of printed circuit fabrication process, the misalignment could be effectively circumvented while enabling flexible extension for multiple conformal and stacking designs. We first conduct validation on the fashion-MNIST dataset and experimentally build up a system using the proposed network architecture for direct recognition of different geometry structures in the electromagnetic space. We envision that the presented architecture, once combined with the advanced dynamic maneuvering techniques and flexible topology, would exhibit unlimited potentials in the areas of high-performance computing, wireless sensing, and flexible wearable electronics.

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Section: Principlesmentioning

confidence: 99%

Direct electromagnetic information processing with planar diffractive neural network

Gu,

Ma,

Gao

et al. 2024

Sci. Adv.

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Photonic online learning: a perspective

et al. 2023

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Emerging neuromorphic hardware promises to solve certain problems faster and with higher energy efficiency than traditional computing by using physical processes that take place at the device level as the computational primitives in neural networks. While initial results in photonic neuromorphic hardware are very promising, such hardware requires programming or “training” that is often power-hungry and time-consuming. In this article, we examine the online learning paradigm, where the machinery for training is built deeply into the hardware itself. We argue that some form of online learning will be necessary if photonic neuromorphic hardware is to achieve its true potential.

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A review of emerging trends in photonic deep learning accelerators

Atwany,

Pardo,

Serunjogi

et al. 2024

Front. Phys.

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Deep learning has revolutionized many sectors of industry and daily life, but as application scale increases, performing training and inference with large models on massive datasets is increasingly unsustainable on existing hardware. Highly parallelized hardware like Graphics Processing Units (GPUs) are now widely used to improve speed over conventional Central Processing Units (CPUs). However, Complementary Metal-oxide Semiconductor (CMOS) devices suffer from fundamental limitations relying on metallic interconnects which impose inherent constraints on bandwidth, latency, and energy efficiency. Indeed, by 2026, the projected global electricity consumption of data centers fueled by CMOS chips is expected to increase by an amount equivalent to the annual usage of an additional European country. Silicon Photonics (SiPh) devices are emerging as a promising energy-efficient CMOS-compatible alternative to electronic deep learning accelerators, using light to compute as well as communicate. In this review, we examine the prospects of photonic computing as an emerging solution for acceleration in deep learning applications. We present an overview of the photonic computing landscape, then focus in detail on SiPh integrated circuit (PIC) accelerators designed for different neural network models and applications deep learning. We categorize different devices based on their use cases and operating principles to assess relative strengths, present open challenges, and identify new directions for further research.

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Hybrid training of optical neural networks

Cited by 4 publications

References 36 publications

Direct electromagnetic information processing with planar diffractive neural network

Direct electromagnetic information processing with planar diffractive neural network

Photonic online learning: a perspective

A review of emerging trends in photonic deep learning accelerators

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