2021
DOI: 10.3390/app11136232
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Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators

Abstract: Reconfigurable linear optical processors can be used to perform linear transformations and are instrumental in effectively computing matrix–vector multiplications required in each neural network layer. In this paper, we characterize and compare two thermally tuned photonic integrated processors realized in silicon-on-insulator and silicon nitride platforms suited for extracting feature maps in convolutional neural networks. The reduction in bit resolution when crossing the processor is mainly due to optical lo… Show more

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Cited by 28 publications
(20 citation statements)
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“…Other impressive works of large-scale photonic integrated circuits can be found in the literature, however they are either non-universal [37][38][39][40][41][42][43][44][45][46][47][48][49], or have unknown performance in terms of losses [50][51][52][53][54][55][56][57][58][59][60]. [36] [20] This work [35] Silica SOI Si 3 N 4 FIG.…”
Section: Discussionmentioning
confidence: 99%
“…Other impressive works of large-scale photonic integrated circuits can be found in the literature, however they are either non-universal [37][38][39][40][41][42][43][44][45][46][47][48][49], or have unknown performance in terms of losses [50][51][52][53][54][55][56][57][58][59][60]. [36] [20] This work [35] Silica SOI Si 3 N 4 FIG.…”
Section: Discussionmentioning
confidence: 99%
“…This necessitates the use of powerhungry optical amplification devices [7] and higher laser power at the input. Uncertainties due to fabrication-process variations-the analysis of which is beyond the scope of this paper-in the two DCs in an MZI can degrade the extinction ratio (ER) of the device which, in turn, will increase the loss and crosstalk in the output [15]. Yet, there is no prior work that analyzes the impact of optical loss and crosstalk noise in SP-NNs.…”
Section: Related Prior Workmentioning
confidence: 99%
“…Yet, there is no prior work that analyzes the impact of optical loss and crosstalk noise in SP-NNs. While the use of silicon nitride platform can help reduce the loss [15], the performance degradation due to coherent crosstalk in SP-NNs still remains unaddressed. Unlike in SP-NNs, optical loss and crosstalk noise have been widely studied in chip-scale Datacom photonic networks (e.g., [8] and [16]), showing signal integrity degradation and scalablity constraints in these networks due to optical loss and crosstalk noise.…”
Section: Related Prior Workmentioning
confidence: 99%
“…In the roadmap towards low power and high density MAC engines, neuromorphic photonics promise to bring sub-fJ per MAC power efficiency with high compactness, while relying on an inherently parallel hardware that reduces the complexity growth [40]. Nevertheless, several challenges must be tackled to enable effective all-optical approaches for neuromorphic hardware, including the efficient largescale integration of many active and passive devices, and the reduction of losses and impairments, which may cause a significant accuracy drop (up to 70% in Mach-Zehnderbased coherent approaches) [41,42]. While considerable effort is put to overcome these issues [17,43,44], photonic analog processors are also emerging within hybrid photonic-electronic accelerators, being particularly suited to perform high-speed MAC operations for reduced-precision ANNs [14,23].…”
Section: A Reduced-precision Computing In Annmentioning
confidence: 99%