Analysis of Optical Loss and Crosstalk Noise in MZI-Based Coherent Photonic Neural Networks

Shafiee, Amin; Banerjee, Sanmitra; Chakrabarty, Krishnendu; Pasricha, Sudeep; Nikdast, Mahdi

doi:10.1109/jlt.2024.3373250

Cited by 4 publications

References 42 publications

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An Integrated All‐Optical Ising Machine with Unlimited Spin Array Size and Coupling

Lan,

Chen,

Gao

et al. 2024

Advanced Photonics Research

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Artificial spin systems are used to solve combinatorial optimization problems by mapping them to the ground state search of the Ising model. Ising machines of various designs, in fields as diverse as photonics and electronics, have been proposed and demonstrated in recent years. One important mathematical operation required in photonic Ising machines for nearly all Hamiltonian minimization algorithms is repeated matrix–vector multiplication with the vector size limited by the physical size of the optical system. Herein, an integrated photonic Ising machine based on matrix partitioning and phase encoding, which effectively allows the use of physically small optical circuits to handle arbitrarily large spin vectors, is proposed. All the complicated calculations are carried out optically, rather than electronically, in this approach. How the required surface area of a hypothetical chip‐based photonic Ising machine can be resized according to convenience, with the trade‐off being the solution time, and that this trade‐off can be done in a way that does not impact the algorithm's efficacy, is shown. Through simulation, the system is predicted to have a high success rate, high noise tolerance, and high error tolerance.

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An Integrated All‐Optical Ising Machine with Unlimited Spin Array Size and Coupling

Lan,

Chen,

Gao

et al. 2024

Advanced Photonics Research

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Resiliency Approaches in Convolutional, Photonic, and Spiking Neural Networks

Bosio,

Gomes,

Pavanello

et al. 2024

2024 IEEE 25th Latin American Test Symposium (LATS)

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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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Analysis of Optical Loss and Crosstalk Noise in MZI-Based Coherent Photonic Neural Networks

Cited by 4 publications

References 42 publications

An Integrated All‐Optical Ising Machine with Unlimited Spin Array Size and Coupling

An Integrated All‐Optical Ising Machine with Unlimited Spin Array Size and Coupling

Resiliency Approaches in Convolutional, Photonic, and Spiking Neural Networks

A review of emerging trends in photonic deep learning accelerators

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