Optoelectronic neural processor for smart vision applications

Lamela, Horacio; Ruiz-Llata, Marta

doi:10.1179/174313107x176243

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…Furthermore, ONNs exhibit promise in areas like medical diagnostics, where the swift and precise analysis of medical imaging data is essential for timely diagnosis and treatment planning. Leveraging their parallel processing capability enhances the speed and accuracy of interpreting medical images, thereby improving healthcare outcomes [ 163 , 164 ]. Moreover, these networks hold potential in communication and data processing networks, offering high bandwidth and low power consumption, thereby enhancing the efficiency of data transmission and processing.…”

Section: Discussion On Pnns and Concluding Remarksmentioning

confidence: 99%

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

Khonina,

Kazanskiy,

Skidanov

et al. 2024

Nanomaterials

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Photonic neural networks (PNNs), utilizing light-based technologies, show immense potential in artificial intelligence (AI) and computing. Compared to traditional electronic neural networks, they offer faster processing speeds, lower energy usage, and improved parallelism. Leveraging light’s properties for information processing could revolutionize diverse applications, including complex calculations and advanced machine learning (ML). Furthermore, these networks could address scalability and efficiency challenges in large-scale AI systems, potentially reshaping the future of computing and AI research. In this comprehensive review, we provide current, cutting-edge insights into diverse types of PNNs crafted for both imaging and computing purposes. Additionally, we delve into the intricate challenges they encounter during implementation, while also illuminating the promising perspectives they introduce to the field.

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Section: Discussion On Pnns and Concluding Remarksmentioning

confidence: 99%

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

Khonina,

Kazanskiy,

Skidanov

et al. 2024

Nanomaterials

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“…These include digital [32–34], analog [35,36], hybrid [37,38], FPGA based [39–41], and (non-electronic) optical implementations [42–44]. …”

Section: Future Implementation: Hardware Neural Networkmentioning

confidence: 99%

“…To address the challenge of mapping highly irregular and non-planar interconnection topology entailing complex computations and distributed communication a wide spectrum of technologies and architectures have been explored in the past. These include digital [ 32 – 34 ], analog [ 35 , 36 ], hybrid [ 37 , 38 ], FPGA based [ 39 – 41 ], and (non-electronic) optical implementations [ 42 – 44 ].…”

Section: Future Implementation: Hardware Neural Networkmentioning

confidence: 99%

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

Juez

Lasheras

Roqueñí

et al. 2012

Sensors

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In astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A).

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“…In order to make full use of this feature, hardware implementation is required. A number of surveys linked to the parallel implementation of neural network in hardware can be found, including digital chips [13], analog chips [14], hybrid chips [15], FPGA based chips [16], and (non-electronic) optical chips [17] implementations.…”

Section: Introductionmentioning

confidence: 99%

FPGA implementation of K-Winners-Take-All neural network based on linear programming formulation

Zhang

2016

2016 Eighth International Conference on Advanced Computational Intelligence (ICACI)

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K-Winner-take-all (kWTA) is an operation that identifies the k largest inputs from multiple input signals. It has important applications in machine learning, statistics filtering and sorting, etc. As the number of inputs becomes large and the selection process should be operated in real time, parallel algorithms are desirable. For these reasons, many neural network algorithms have been proposed to solve kWTA. Compared with software simulations, the hardware implementation is capable of a high degree of parallelism. There are many hardware implementations that have been proposed, such as digital chips, analog chips, hybrids chips, FPGA based chips, and (non-electronic) optical chips implementation. Compared with other hardware implementations, the FPGA provides an effective programmable resource, together with a fast prototyping and rapid system deployment. In this paper, a new hardware implementation technique for a typical neural network of kWTA using a field-programmablegate-array (FPGA) chip is proposed. Experimental results show that the proposed hardware implementation method has a high degree of parallelism and fast performance.

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Optoelectronic neural processor for smart vision applications

Cited by 8 publications

References 31 publications

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

FPGA implementation of K-Winners-Take-All neural network based on linear programming formulation

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