Optical pattern generator for efficient bio-data encoding in a photonic sequence comparison architecture

Abadi, Saeedeh Akbari Rokn; Dijujin, Negin Hashemi; Koohi, Somayyeh

doi:10.1371/journal.pone.0245095

Cited by 6 publications

(9 citation statements)

References 29 publications

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“…where, Φ = Af(T). To normalize (10), we define a dimensionless variable representing the linear absorption as Z = σ01Nz. Thus, we can write:…”

Section: ) Atomic Model Of Saturable Absorption Nonlinearitymentioning

confidence: 99%

“…In particular, convolutional neural networks (CNNs) increase performance of computer vision applications, namely image classification [5] and pattern recognition [6]. Notwithstanding the fact that CNNs dramatically affect the performance of solving NP-complete problems, they afflict by massive computations due to quite a few parameters and operations in neural networks [7]- [9] which have huge datasets as biomedical applications [10].…”

Section: Introductionmentioning

confidence: 99%

“…As another important usage of optical implementation of CNN, optical processing of large biological data sequences can be mentioned. As discussed in [10], by utilizing optical correlator system as a sequence alignment tool for biological sequences (i.e. DNAs, RNAs, or proteins) we can speeds up biological data comparison by more than 81%, against its electrical counterparts.…”

Section: Introductionmentioning

confidence: 99%

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Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations

2021

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Despite various optical realizations of convolutional neural networks (CNNs), optical implementation of nonlinear activation functions and pooling operations are still challenging problems. In this regard, this paper proposes an optical saturable absorption nonlinearity and its atomic-level model, as well as two various optical pooling operations, namely optical average pooling and optical motion pooling, by means of 4f optical correlators. Proposing these optical building blocks not only speed up the neural networks due to negligible optical processing latency, but also facilitate the concatenation of optical convolutional layers with no optoelectrical conversions in-between, as the significant bottlenecks of implementing photonic CNNs. Furthermore, the proposed optical motion pooling layer increases the translation invariance property of CNNs, avoiding the inclusion of all corresponding translated images for the training procedure, and hence, increases the training speed of the neural network. The classification accuracy of the proposed optical convolutional layer is evaluated as the first layer of a customized version of AlexNet architecture, named as OP-AlexNet, for classification of Kaggle Cats and Dog challenge, CIFAR-10, and MNIST datasets, as 83.76%, 72.82%, and 99.25%, respectively, by using optical average pooling.

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“…where, Φ = Af(T). To normalize (10), we define a dimensionless variable representing the linear absorption as Z = σ01Nz. Thus, we can write:…”

Section: ) Atomic Model Of Saturable Absorption Nonlinearitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations

2021

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“…Over 50 years have passed and the Vander Lugt correlator remains an effective tool for pattern recognition problems such alignment of deoxyribonucleic acid (DNA) sequences [23] or counting k-mers substrings in a DNA string [24]. Pattern recognition can also be achieved via optical artificial neural networks (ANNs) [25,26].…”

Section: Introductionmentioning

confidence: 99%

Holographic photonic neuron

Daria

2021

Neuromorph. Comput. Eng.

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The promise of artificial intelligence (AI) to process complex datasets has brought about innovative computing paradigms. While recent developments in quantum-photonic computing have reached significant feats, mimicking our brain’s ability to recognize images are poorly integrated in these ventures. Here, I incorporate orbital angular momentum (OAM) states in a classical Vander Lugt optical correlator to create the holographic photonic neuron (HoloPheuron). The HoloPheuron can memorize an array of matched filters in a single phase-hologram, which is derived by linking OAM states with elements in the array. Successful correlation is independent of intensity and yields photons with OAM states of lℏ, which can be used as a transmission protocol or qudits for quantum computing. The unique OAM identifier establishes the HoloPheuron as a fundamental AI device for pattern recognition that can be scaled and integrated with other computing platforms to build-up a neuromorphic quantum-photonic processor that mimics the brain

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“…It is worth noted that optical implementation of CNN can be used in real-life applications, such as processing of large biological data sequences. As explained in [30], while designing sequence alignment tool for biological sequences (i.e., DNAs, RNAs, or proteins) is a debatable issue, adopting optical correlators can speed up the computation time by 81% against the electrical counterparts.…”

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confidence: 99%

High-Speed Multi-Layer Convolutional Neural Network Based on Free-Space Optics

Sadeghzadeh

Koohi

2022

IEEE Photonics J.

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Convolutional neural networks (CNNs) are at the heart of several machine learning applications, while they suffer from computational complexity due to their large number of parameters and operations. Recently, all-optical implementation of the CNNs has achieved many attentions, however, the recently proposed optical architectures for CNNs cannot fully utilize the tremendous capabilities of optical processing, due to the required electro-optical conversions in-between successive layers. To implement an all-optical multi-layer CNN, it is essential to optically implement all required operations, namely convolution, summation of channels' output for each convolutional kernel feeding the nonlinear unit, nonlinear activation function, and finally, pooling operations. Considering the lack of multi-layer photonic CNN implementation, in this paper, we explore a fully-optical design for implementing successive convolutional layers in an optical CNN. As a proof of concept, and without loss of generality, we considered two successive optical layers in the proposed network, named as 2L-OPCNN, for comparative studies against electrical counterpart and single optical layer CNN. Our simulation results confirm nearly the same accuracies for classifying images of Kaggle Cats and Dogs challenge, CIFAR-10, and MNIST datasets, compared to the electrical counterpart, as well as improved accuracies compared to single optical layer CNN.

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Optical pattern generator for efficient bio-data encoding in a photonic sequence comparison architecture

Cited by 6 publications

References 29 publications

Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations

Free-Space Optical Neural Network Based on Optical Nonlinearity and Pooling Operations

Holographic photonic neuron

High-Speed Multi-Layer Convolutional Neural Network Based on Free-Space Optics

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