Deterministic conversion rule for CNNs to efficient spiking convolutional neural networks

Yang, Xu; Zhang, Zhongxing; Zhu, Wenping; Yu, Shuangming; Liu, Liyuan; Wu, Nanjian

doi:10.1007/s11432-019-1468-0

Cited by 24 publications

(18 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this end, synaptic weights of neurons belonging to the same map should always be the same. As for the PL provide translation invariance using maximum operation, and also help the network to compress the flow of visual data [58] . Neurons in PLs propagate the first spike received from neighboring neurons in the previous layer which are selective to the same feature [57] [58].…”

Section: Spiking Cnnmentioning

confidence: 99%

“…As for the PL provide translation invariance using maximum operation, and also help the network to compress the flow of visual data [58] . Neurons in PLs propagate the first spike received from neighboring neurons in the previous layer which are selective to the same feature [57] [58]. The Spiking CNN's CL and PL are arrange in consecutive order if the magnitude of FE is needed further.…”

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%

“…Each CL extracts features through spiking convolution process. 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%

“…As for the CNN component integration with DL-LSC-ESNN, the integration aforementioned argument is on its reliable FE performance. CNN's FE ability together with SC's ROP integration eventually lead to tougher translational invariances immunity [58] . This distinctive ability and performance leads to the combination of all component and strengthens DL-LSC-ESNN adaptability to stochastic nature of HVM.…”

Section: Proposed Deep Learning Laser Speckle Contrast Evolving Smentioning

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

View full text Add to dashboard Cite

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.

show abstract

Section: Spiking Cnnmentioning

confidence: 99%

Section: Spiking Cnnmentioning

confidence: 99%