SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology

Dey, Somdip; Singh, Amit Kumar; Prasad, Dilip K.; McDonald-Maier, Klaus

doi:10.1109/access.2019.2949483

Cited by 27 publications

(17 citation statements)

References 36 publications

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“…Convolutional neural networks (CNNs) have achieved stateof-the-art performance in a myriad of tasks, such as computer vision [38] and neuroimaging [39], due to their superb abilities to learn and analyze spatially correlated patterns. Their successes have also been shared in EEG signal processing but with the requirement of high parameter models, which consume excessive training time, and have not been shown to generalize across subjects, making them infeasible for BCI implementation.…”

Section: ) Support Vector Machine (Baseline Model)mentioning

confidence: 99%

Robust Subject-Independent P300 Waveform Classification via Signal Pre-Processing and Deep Learning

Sahay

Brinton

2021

IEEE Access

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Brain Computer Interfaces (BCIs) are capable of processing neural stimuli using electroencephalogram (EEG) measurements to aid communication capabilities. Yet, BCIs often require extensive calibration steps in order to be tuned to specific users. In this work, we develop a subject independent P300 classification framework, which eliminates the need for user-specific calibration. We begin by employing a series of pre-processing steps, where, among other steps, we consider different trial averaging methodologies and various EEG electrode configurations. We then consider three distinct deep learning architectures and two linear machine learning models as P300 signal classifiers. Through evaluation on three datasets, and in comparison to three benchmark P300 classification frameworks, we find that averaging up to seven trials while using eight specific electrode channels on a two-layered convolutional neural network (CNN) leads to robust subject independent P300 classification. In this capacity, our method achieves greater than a 0.20 gain in AUC in comparison to prior P300 classification methods. In addition, our proposed framework is computationally efficient with training time gains of greater than 3x, compared to linear machine learning models, and online evaluation time speedups of up to 2x compared to benchmark methods.

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Section: ) Support Vector Machine (Baseline Model)mentioning

confidence: 99%

Robust Subject-Independent P300 Waveform Classification via Signal Pre-Processing and Deep Learning

Sahay

Brinton

2021

IEEE Access

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“…To improve energy consumption and/or to meet performance constraint in multi-core mobile platforms, various approaches for DVFS and/or mapping have been proposed using offline, online or hybrid (online optimization facilitated by offline analysis results) optimization for resource management [1], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34]. Depending on the control mechanism, runtime energy management approaches can be further classified into two categories: proactive [21], [22], [30] and reactive [16], [29], [31], [33], [34], [35], [36].…”

Section: Dynamic Energy Managementmentioning

confidence: 99%

“…In [34], a new approach for dynamic power management is proposed, where the program source code of the executing application is automatically converted to LLVM intermediate representation (IR) code. The IR code is consecutively converted to a machine readable image, which is used for classification by a CNN model in to either of the following categories: compute intensive (the program is compute intensive), memory intensive (the program is memory intensive) and mixed load (the program is both compute and memory intensive).…”

Section: Reactive Approachesmentioning

confidence: 99%

“…The IR code is consecutively converted to a machine readable image, which is used for classification by a CNN model in to either of the following categories: compute intensive (the program is compute intensive), memory intensive (the program is memory intensive) and mixed load (the program is both compute and memory intensive). Based on the classification of the program source code by CNN in [34], DVFS is utilized on the multi-core mobile platform to dynamically optimize power. This work was evaluated on Samsung Exynos 5422 MPSoC.…”

Section: Reactive Approachesmentioning

confidence: 99%

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Dynamic Energy and Thermal Management of Multi-core Mobile Platforms: A Survey

et al. 2020

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Multi-core mobile platforms are on rise as they enable efficient parallel processing to meet ever-increasing performance requirements. However, since these platforms need to cater for increasingly dynamic workloads, efficient dynamic resource management is desired mainly to enhance the energy and thermal efficiency for better user experience with increased operational time and lifetime of mobile devices. This article provides a survey of dynamic energy and thermal management approaches for multi-core mobile platforms. These approaches do either proactive or reactive management. The upcoming trends and open challenges are also discussed.

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“…For our traffic categorization experimentation we are using the same dataset used in [3], [7]. Hardware setup: We have implemented the methodology on an Odroid-XU4 [16], [23], which employs Exynos 5422 MPSoC [24], [25], [26] used in popular Samsung Note phones and phablets. Experimental Results: To categorize traffic we chose four pre-Trained CNN models, which were trained on millions of ImageNet images, for our validation.…”

Section: Dataset Usedmentioning

confidence: 99%

Temporal Motionless Analysis of Video using CNN in MPSoC

Dey¹,

Singh²,

Prasad³

et al. 2020

Preprint

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<div><div><div><p>This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis of videos) on mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image (ECTI) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption of the device.</p></div></div></div>

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SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology

Cited by 27 publications

References 36 publications

Robust Subject-Independent P300 Waveform Classification via Signal Pre-Processing and Deep Learning

Robust Subject-Independent P300 Waveform Classification via Signal Pre-Processing and Deep Learning

Dynamic Energy and Thermal Management of Multi-core Mobile Platforms: A Survey

Temporal Motionless Analysis of Video using CNN in MPSoC

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