Unified Model for Contrast Enhancement and Denoising

James, Alex Pappachen; Krestinskaya, Olga

doi:10.1109/isvlsi.2017.73

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…The exponential component of the algorithm is replaced by the threshold logic based decision making process, which simplifies the implementation of the activation function. The dot product multiplication operation is performed in the memristive crossbar array, which has been proven to be efficient for various neuromorhcic and neural network architectures [3,4,5,6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Approximate Probabilistic Neural Networks with Gated Threshold Logic

Krestinskaya

James

2018

2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO)

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Probabilistic Neural Network (PNN) is a feed-forward artificial neural network developed for solving classification problems. This paper proposes a hardware implementation of an approximated PNN (APNN) algorithm in which the conventional exponential function of the PNN is replaced with gated threshold logic. The weights of the PNN are approximated using a memristive crossbar architecture. In particular, the proposed algorithm performs normalization of the training weights, and quantization into 16 levels which significantly reduces the complexity of the circuit.

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Section: Introductionmentioning

confidence: 99%

Approximate Probabilistic Neural Networks with Gated Threshold Logic

Krestinskaya

James

2018

2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO)

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“…This problem can be addressed using memristor based analog circuits. Memristor has been proven to be useful for various object detection systems [4], [5], edge detection [6] and image filtering applications [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Analog Pixel-to-Pixel Dynamic Frame Differencing with Memristive Sensing Circuits

Krestinskaya¹,

James²

2018

2018 Ieee Sensors

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In this paper, we propose an analog pixel differencing circuit for differentiating pixels between frames directly from CMOS pixels. The analog information processing at sensor is a topic of growing appeal to develop edge AI devices. The proposed circuit is integrated into a pixel-parallel and pixel-column architectures. The proposed system is design using TSMC 180nm CMOS technology. The power dissipation of the proposed circuit is 96.64mW , and on-chip ares is 531.66µm 2 . The architectures are tested for moving object detection application.

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“…In addition, due to the limitations of the levels of the memristive devices, BNN hardware implementation can be built with the existing binary memristive devices. The memristive devices have been proven to be efficient for various architectures in terms of scalability, power consumption and on-chip area [3,4,5,6,7,8,9,10,11,12].…”

mentioning

confidence: 99%

Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing

Krestinskaya

James

2018

2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO)

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The memristive crossbar aims to implement analog weighted neural network, however, the realistic implementation of such crossbar arrays is not possible due to limited switching states of memristive devices. In this work, we propose the design of an analog deep neural network with binary weight update through backpropagation algorithm using binary state memristive devices. We show that such networks can be successfully used for image processing task and has the advantage of lower power consumption and small on-chip area in comparison with digital counterparts. The proposed network was benchmarked for MNIST handwritten digits recognition achieving an accuracy of approximately 90%.

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Unified Model for Contrast Enhancement and Denoising

Cited by 3 publications

References 18 publications

Approximate Probabilistic Neural Networks with Gated Threshold Logic

Approximate Probabilistic Neural Networks with Gated Threshold Logic

Real-Time Analog Pixel-to-Pixel Dynamic Frame Differencing with Memristive Sensing Circuits

Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing

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