Optimizing Coherent Integrated Photonic Neural Networks under Random Uncertainties

Banerjee, Sanmitra; Nikdast, Mahdi; Chakrabarty, Krishnendu

doi:10.1364/ofc.2021.th1a.22

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…Several optical neural networks based on silicon photonic integrated circuits have been proposed, including coherent networks using MZIs [78], [79], [80], [81] and noncoherent networks using MRR banks [77], [82], [83], [84], [85], [86], [87], hence enhancing the hardware implementation of multiplication units with higher speed and lower computation energy consumption compared to electronic counterparts. Despite being beneficial compared to electronic accelerators, photonic AI accelerators suffer from inherent limitations, including optical loss and crosstalk noise [75], large footprint (≈1000 µm for a deep neural network with two hidden layers), and sensitivity to thermal and process variations, which result in deterioration of the system's overall performance (e.g., drop in inferencing accuracy) as the network scales up [88], [89], [90], and [91].…”

Section: Phase Change Materials For Photonic In-memory Computingmentioning

confidence: 99%

A Survey on Optical Phase-Change Memory: The Promise and Challenges

2023

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Silicon photonics (SiPh) technology has facilitated the deployment of integrated photonics across different application domains, from ultra-fast communication in Datacom applications to energy-efficient optical computation in emerging hardware accelerators for machine learning. More recently, the integration of SiPh and phase change materials has created a unique opportunity to realize adaptable, reconfigurable, and programmable photonic platforms. In particular, the nonvolatile programmability in phase change materials has made them a promising candidate for implementing photonic memory cells and architectures. Accordingly, photonic memory systems and even in-memory photonic computing paradigms are on the rise, especially given their potential for improving data access in electronic and photonic processors. However, there are still many challenges in the design and fabrication of phase-change photonic integrated circuits, which need to be addressed. This article presents a comprehensive survey on the recent advances and challenges for the integration of phase change materials with contemporary photonic devices while focusing on the photonic memory application. In particular, we explore phase-change photonic memory from the material level to the architecture level by presenting an overview of different material-level characteristics of phase change materials with their optical, electrical, and thermal properties as well as their integration into SiPh devices and photonic memory architectures and their application for in-memory photonic computing. We also present a comparison with electronic memory and discuss open research challenges that must be addressed to further advance phase-change photonic memory towards successful integration into emerging computing systems.INDEX TERMS Phase change materials, phase-change memory, in-memory photonic computing, photonic integrated circuits, silicon photonics.

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Section: Phase Change Materials For Photonic In-memory Computingmentioning

confidence: 99%

A Survey on Optical Phase-Change Memory: The Promise and Challenges

2023

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“…A method was presented in [6] to counter the impact of both FPVs and thermal effects using modified cost functions during training with added benefits of post-fabrication hardware calibration. The impact of FPVs can also be reduced by minimizing the tuned phase angles in an SPNN; this can be done by leveraging the non-uniqueness of SVD [13] or by pruning redundant phase angles [14], [15]. All these methods focus on mitigating deviations in SPNNs post-fabrication by either using thermal actuators or by post-fabrication training methods to compensate for any additionally introduced phase noise.…”

Section: Related Work On Fpv Analysis In Spnnsmentioning

confidence: 99%

Characterization and Optimization of Integrated Silicon-Photonic Neural Networks under Fabrication-Process Variations

Mirza¹,

Shafiee²,

Banerjee³

et al. 2022

Preprint

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Silicon-photonic neural networks (SPNNs) have emerged as promising successors to electronic artificial intelligence (AI) accelerators by offering orders of magnitude lower latency and higher energy efficiency. Nevertheless, the underlying silicon photonic devices in SPNNs are sensitive to inevitable fabrication-process variations (FPVs) stemming from optical lithography imperfections. Consequently, the inferencing accuracy in an SPNN can be highly impacted by FPVs-e.g., can drop to below 10%-the impact of which is yet to be fully studied. In this paper, we, for the first time, model and explore the impact of FPVs in the waveguide width and silicon-on-insulator (SOI) thickness in coherent SPNNs that use Mach-Zehnder Interferometers (MZIs). Leveraging such models, we propose a novel variation-aware, design-time optimization solution to improve MZI tolerance to different FPVs in SPNNs. Simulation results for two example SPNNs of different scales under realistic and correlated FPVs indicate that the optimized MZIs can improve the inferencing accuracy by up to 93.95% for the MNIST handwritten digit dataset-considered as an example in this paper-which corresponds to a <0.5% accuracy loss compared to the variation-free case. The proposed one-time optimization method imposes low area overhead, and hence is applicable even to resource-constrained designs.

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Silicon Photonics for Future Computing Systems

Shafiee

Pasricha

Nikdast

2022

Wiley Encyclopedia of Electrical and Electronics Engineering

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The primary goal of this article is to provide an overview of silicon photonics technology and its applications in the design and improvement of current and future computing systems. We start by reviewing silicon photonics technology and introducing some of its benefits and challenges as well as providing some background on it. Next, we introduce some fundamental silicon photonic components in the design of silicon photonic integrated circuits (PICs) and optical interconnect for computing systems as well as their operating principles and applications. These components can be active, such as photodetectors and optical modulators, or passive, such as silicon‐on‐insulator (SOI) waveguides. Subsequently, we discuss the application of silicon photonics to improve the communication and computation infrastructure in future computing systems, while reviewing the state‐of‐the‐art and some design and implementation challenges. Finally, we discuss several research opportunities to push forward the application of silicon photonics in the design of future computing systems.

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Optimizing Coherent Integrated Photonic Neural Networks under Random Uncertainties

Abstract: We propose an optimization method to improve power efficiency and robustness in silicon-photonic-based coherent integrated photonic neural networks. Our method reduces the network power consumption by 15.3% and the accuracy loss under uncertainties by 16.1%.

Cited by 9 publications

References 5 publications

A Survey on Optical Phase-Change Memory: The Promise and Challenges

A Survey on Optical Phase-Change Memory: The Promise and Challenges

Characterization and Optimization of Integrated Silicon-Photonic Neural Networks under Fabrication-Process Variations

Silicon Photonics for Future Computing Systems

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