High Speed, Approximate Arithmetic Based Convolutional Neural Network Accelerator

Elbtity, Mohammed; Son, Hyun-Wook; Lee, Dong Hoon; Kim, HyungWon

doi:10.1109/isocc50952.2020.9333013

Cited by 7 publications

(6 citation statements)

References 2 publications

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“…Figure 9(a) shows that the entropy values of Cz, T3, T4, T6, and other channels are lower as compared to other channels, indicating that the classifier establishes the fundamental differentiation between depression and health using voltage-amplitude channels. There are considerable disparities in depressed pathways between the left and right temporal lobes as depicted by the three-dimensional scalp topographic map in Figure 9(b) , which supports the data provider's pathological diagnosis [ 8 ].…”

Section: Experimental Setup and Results Analysissupporting

confidence: 58%

“…EEG data from patients with severe depression and healthy controls are included in the public dataset [ 8 ]. The study also included a healthy control group, which was not subject to any mental or physical illness.…”

Section: Experimental Setup and Results Analysismentioning

confidence: 99%

“…Different classifiers were used to the same dataset (MPHC EEG data) for depressed state classification, and their classification performance metrics are provided in Table 1 . Along with MLRW [ 8 ] this article implemented numerous exemplary neural network models, including Resnet-16 [ 19 ], CapsuleNet [ 20 ], and LeNet [ 21 ]. All of these classifiers were updated somewhat.…”

Section: Experimental Setup and Results Analysismentioning

confidence: 99%

“…The majority of current research is focused on feature extraction, and machine learning techniques have exploded in popularity in recent years. The leading position in this direction is as follows: The literature [ 8 ] proposed a classification method based on wavelet transform-based time-frequency decomposition that had an 87.5 percent diagnostic accuracy for MDD patients and healthy controls. To efficiently detect the diverse lesions associated with severe depression, a technique for extracting spectral-spatial features from EEG data was devised, which attained an average accuracy of 81.23 percent [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

Thakare¹,

Bhende²,

Deb

et al. 2022

BioMed Research International

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Patients suffering from severe depression may be precisely assessed using online EEG categorization and their progress tracked over time, minimizing the risk of danger and suicide. Online EEG categorization systems, on the other hand, suffer additional challenges in the absence of empirical oversight. A lack of effective decoupling between brain regions and neural networks occurs during brain disease attacks, resulting in EEG data with poor signal intensity, high noise, and nonstationary characteristics. CNN employs momentum SGD optimization. By using a tiny momentum decay factor, the literature’s starting strategy, and the same batch normalization, this work attempts to decrease model error. Before being utilized to form a training set, samples are shuffled, followed by validation and testing on the new samples in the set. An online EEG categorization system driven by a convolution neural network has been developed to do this. The approach is applied directly to the EEG input and is able to accurately and quickly identify depressed states without the need for preprocessing or feature extraction. The healthy control group and the depression control group had accuracy, sensitivity, and specificity of 99.08 percent, 98.77 percent, and 99.42 percent, respectively, in experiments on depression evaluation based on publicly accessible data. The machine learning technique based on feature extraction is often getting more and more complex, making it only suited for offline EEG categorization. While neural networks have become increasingly important in the study of artificial intelligence in recent years, they are still essentially black-box function approximations with limited interpretability. In addition, quantitative study of the neural network shows that depressed patients and healthy persons have remarkable dissimilarity between the right and left temporal lobe brain regions.

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Section: Experimental Setup and Results Analysissupporting

confidence: 58%

Section: Experimental Setup and Results Analysismentioning

confidence: 99%

Section: Experimental Setup and Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

Thakare¹,

Bhende²,

Deb

et al. 2022

BioMed Research International

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“…The results showed considerable energy reduction with a minor reduction in the classification accuracy when evaluated using the MNIST and the Canadian Institute For Advanced Research (CIFAR-10) [6] datasets. Meanwhile, a small area, low power and high-speed CNN architecture was proposed in [7]. Approximate MAC units (Multiply and Accumulate) for convolution and fully connected layers while using parallel memory access were introduced.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Precision-Scaled CNN Implementation With Dynamic Partial Reconfiguration

et al. 2022

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A convolutional neural network (CNN) classifies images with high accuracy. However, CNN operation requires a large number of computations which consume a significant amount of power when implemented on hardware. Precision scaling has been recently used to reduce the hardware requirements and power consumption. In this paper, we present an energy-efficient precision-scaled CNN (EEPS-CNN) architecture. Furthermore, the Field Programmable Gate Array (FPGA) is reconfigured during run time using Dynamic Partial Reconfiguration (DPR). If the battery level decreases, the EEPS-CNN design with the most appropriate power consumption is configured on the FPGA. DPR enables recognition applications to run at a low power budget while sacrificing minor accuracy instead of termination. The proposed architecture is implemented on Xilinx XC7Z020 FPGA and is evaluated on three datasets: MNIST, F-MNIST, and SVHN datasets. The results show a 2.2X, 2.39X, and 2.38X reduction in the energy consumption, respectively, while using only 7 bits to represent all inputs and network parameters. The accuracy of the proposed EEPS-CNN is only 0.53%, 3.67%, and 0.88% less than 32-bit floating-point architectures for MNIST, F-MNIST, and SVHN, respectively. Moreover, the results show up to 92.91X and 4.84X reductions in the power and energy consumption of the proposed EEPS-CNN compared to related designs developed for the MNIST dataset.

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Design of Optimized CNN for Image Processing using Verilog

Veena,

Deodurg,

Shrinidhi

et al. 2023

2023 4th IEEE Global Conference for Advancement in Technology (GCAT)

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High Speed, Approximate Arithmetic Based Convolutional Neural Network Accelerator

Cited by 7 publications

References 2 publications

[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

[Retracted] Classification of Bioinformatics EEG Data Signals to Identify Depressed Brain State Using CNN Model

Energy-Efficient Precision-Scaled CNN Implementation With Dynamic Partial Reconfiguration

Design of Optimized CNN for Image Processing using Verilog

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