Rank order coding based spiking convolutional neural network architecture with energy-efficient membrane voltage updates

Tang, Hoyoung; Cho, Donghyeon; Lew, Dongwoo; Kim, Tae‐Hwan; Park, Jongsun

doi:10.1016/j.neucom.2020.05.031

Cited by 15 publications

(6 citation statements)

References 28 publications

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“…As for the spike train encoding, this study utilizing ROP. The ROP is being chosen due to drawbacks of conventional spiking neural network (SNN) long simulation rate coding probabilistic conversion [48] . The ROP is proposed to alleviate this issues by encoding the SC images to pixel-based spikes.…”

Section: A Quantification and Formulation Of Monochromatic Polycarbomentioning

confidence: 99%

“…As the intensity of a stimulus increases, the rate of spikes increases to convey much more important information [52] . To summarize, the ROP true functionality is its ability to generated spikes by sequentially sorting input values from its pre-layers [48] after SC conversion.…”

Section: A Quantification and Formulation Of Monochromatic Polycarbomentioning

confidence: 99%

“…The information in a spiking CNN is transferred via spike trains instead of real values [55] [56] . The spike train conventional generated via rank order or population based [48] . This to ensure that the neurons in CL detect more complex features by integrating input spikes from the previous layer which detects abstract visual features.…”

Section: Spiking Cnnmentioning

confidence: 99%

“…Ultimately the justification from Spiking CNN indicated that the CL are significant due to its ability to extract only important spike generated after ROP conversion [48] [58] . Each CL extracts features through spiking convolution process.…”

Section: Spiking Cnnmentioning

confidence: 99%

“…Then, the pooling layer combines the outputs of neurons cluster in one feature maps (FM) into the input of one neuron in the next layer [58]. Pooling layer (PL) also behaves as a FM reducer by reducing the size of the FM by pooling maximum amplitudes given by CL [48].…”

Section: Spiking Cnnmentioning

confidence: 99%

See 4 more Smart Citations

Hydrofluoroether Impurities—Chemical Detection Using a Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network

et al. 2020

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Hydrofluoroether (HFE) impurities detection is an issue related to detecting chemical contamination within a high volume manufacturing (HVM) chiller caused by a rapid emulated environmental attack. The aftereffects of the cycle emulated attack may eventually create a micro-crack in the heat-exchanger. This event eventually causes the contamination of HFE due to multiple chemical interactions. This study proposes a new classification methodology to detect HFE chemical impurities using induction by a 532nm laser. The purpose of the laser induction is to leverage its laser speckle contrast attributes and amplify its detection. One of the reasons for choosing the 532nm laser spectrum is its highest quantum efficiency. Once amplification of detection is achieved, the detector tends to be in a highly sensitive mode due to low marginality to differentiate between two different conditions. This mode is prone to be stochastic. Thus, a new form of architecture known as Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network (DL-LSC-ESNN) is proposed. The architecture utilizes speckle contrast domain conversion, dimensional additivity of the receptive neuron of evolving spiking neural network (ESNN), followed by the strength of Convolution Neural Network (CNN) feature extraction (FE) capability. Ultimately, the evolving and adaptive ability of ESNN is assimilated and integrated seamlessly. CNN acted to extract only an important spike train, and the result is its essences of important spikes or spike feature maps. The spike feature maps are then fed into the ESNN neuron repository, which either assimilates or creates a new neuron repository. The proposed methods show significant improvement in the accuracy of detection against multiple baseline state of the art CNN architecture and ultimately demonstrated its capability to detect real-time contamination of HFE thus improved the detection rate significantly for the HVM environment.

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Section: A Quantification and Formulation Of Monochromatic Polycarbomentioning

confidence: 99%

Section: A Quantification and Formulation Of Monochromatic Polycarbomentioning

confidence: 99%

Section: Spiking Cnnmentioning

confidence: 99%

Section: Spiking Cnnmentioning

confidence: 99%

Section: Spiking Cnnmentioning

confidence: 99%

See 3 more Smart Citations

Hydrofluoroether Impurities—Chemical Detection Using a Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network

et al. 2020

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A Novel Multi-Type Image Coding Method Acting on Supervised Hierarchical Deep Spiking Convolutional Neural Networks for Image Classification

Liu,

Xu,

Yang

et al. 2024

Cogn Comput

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Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges

Zhong,

Su,

et al. 2024

Nano-Micro Lett.

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Spike-based neural networks, which use spikes or action potentials to represent information, have gained a lot of attention because of their high energy efficiency and low power consumption. To fully leverage its advantages, converting the external analog signals to spikes is an essential prerequisite. Conventional approaches including analog-to-digital converters or ring oscillators, and sensors suffer from high power and area costs. Recent efforts are devoted to constructing artificial sensory neurons based on emerging devices inspired by the biological sensory system. They can simultaneously perform sensing and spike conversion, overcoming the deficiencies of traditional sensory systems. This review summarizes and benchmarks the recent progress of artificial sensory neurons. It starts with the presentation of various mechanisms of biological signal transduction, followed by the systematic introduction of the emerging devices employed for artificial sensory neurons. Furthermore, the implementations with different perceptual capabilities are briefly outlined and the key metrics and potential applications are also provided. Finally, we highlight the challenges and perspectives for the future development of artificial sensory neurons.

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Rank order coding based spiking convolutional neural network architecture with energy-efficient membrane voltage updates

Cited by 15 publications

References 28 publications

Hydrofluoroether Impurities—Chemical Detection Using a Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network

Hydrofluoroether Impurities—Chemical Detection Using a Deep Learning Laser Speckle Contrast Evolving Spiking Neural Network

A Novel Multi-Type Image Coding Method Acting on Supervised Hierarchical Deep Spiking Convolutional Neural Networks for Image Classification

Recent Advances in Artificial Sensory Neurons: Biological Fundamentals, Devices, Applications, and Challenges

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