Analyzing the Resilience of Convolutional Neural Networks Implemented on GPUs: Alexnet as a Case Study

There have been an extensive use of Convolutional Neural Networks (CNNs) in healthcare applications. Presently, GPUs are the most prominent and dominated DNN accelerators to increase the execution speed of CNN algorithms to improve their performance as well as the Latency. However, GPUs are prone to soft errors. These errors can impact the behaviors of the GPU dramatically. Thus, the generated fault may corrupt data values or logic operations and cause errors, such as Silent Data Corruption. unfortunately, soft errors propagate from the physical level (microarchitecture) to the application level (CNN model). This paper analyzes the reliability of the AlexNet model based on two metrics: (1) critical kernel vulnerability (CKV) used to identify the malfunction and light- malfunction errors in each kernel, and (2) critical layer vulnerability (CLV) used to track the malfunction and light-malfunction errors through layers. To achieve this, we injected the AlexNet which was popularly used in healthcare applications on NVIDIA’s GPU, using the SASSIFI fault injector as the major evaluator tool. The experiments demonstrate through the average error percentage that caused malfunction of the models has been reduced from 3.7% to 0.383% by hardening only the vulnerable part with the overhead only 0.2923%. This is a high improvement in the model reliability for healthcare applications

Development and validation of scale using rasch analysis to measure students’ entrepreneurship readiness to learn embedded system design course

Embedded systems are growing rapidly as the technology paves the way for the rise of future of smart manufacturing through a wide range of industries. The intensity demands of innovation required a steady supply of innovative and entrepreneurship engineers to ensures the industry players have a sustainable supply of talent to fuel their growth and investments. The university acknowledge the current and future demand of the labour market by offering embedded system course that are developed to equipped the next generation engineers with innovation and entrepreneurship skills to enable them to turn their ideas into reality. This paper developed and validated a scale to measure the student entrepreneurship skills readiness for embedded systems design course using the Rasch analysis.The content validity results show that CVR is 0.92 and CVI is 0.96 indicating an excellent content validity. The pilot test result show that the scale Cronbach alpha is 0.80 indicating excellent scale reliability. The construct validity of the scale was evaluated using WINSTEPS version 3.92.1, with results indicated that all the items of the scale fit the Rasch model with satisfactoryfit index and showedexcellent consistency, with reliability alpha of 0.99 foe items and 0.75 for persons. The findings depicted that most of the students have poor business and entrepreneurship skills, such as marketing and negotiation abilities. Therefore, higher learning institutions need to embed acquirable entrepreneurial skills in the prerequisites courses to provide adequate training to the students, increasing their creativity and maximizing their potential to be successful entrepreneurs

Global Matrix 3.0 Physical Activity Report Card Grades for Children and Youth:Results and Analysis From 49 Countries

BACKGROUND: Accumulating sufficient moderate to vigorous physical activity is recognized as a key determinant of physical, physiological, developmental, mental, cognitive, and social health among children and youth (aged 5-17 y). The Global Matrix 3.0 of Report Card grades on physical activity was developed to achieve a better understanding of the global variation in child and youth physical activity and associated supports. METHODS: Work groups from 49 countries followed harmonized procedures to develop their Report Cards by grading 10 common indicators using the best available data. The participating countries were divided into 3 categories using the United Nations' human development index (HDI) classification (low or medium, high, and very high HDI). RESULTS: A total of 490 grades, including 369 letter grades and 121 incomplete grades, were assigned by the 49 work groups. Overall, an average grade of "C-," "D+," and "C-" was obtained for the low and medium HDI countries, high HDI countries, and very high HDI countries, respectively. CONCLUSIONS: The present study provides rich new evidence showing that the situation regarding the physical activity of children and youth is a concern worldwide. Strategic public investments to implement effective interventions to increase physical activity opportunities are needed.</p

A selective mitigation technique of soft errors for DNN models used in healthcare applications: DenseNet201 case study

Deep neural networks (DNNs) have been successfully deployed in widespread domains, including healthcare applications. DenseNet201 is a new DNN architecture used in healthcare systems (i.e., presence detection of the surgical tool). Specialized accelerators such as GPUs have been used to speed up the execution of DNNs. Nevertheless, GPUs are prone to transient effects and other reliability threats, which can impact DNN models’ reliability. Safety-critical systems, such as healthcare applications, must be highly reliable because minor errors might lead to severe injury or death. In this paper, we propose a selective mitigation technique that relies on in-depth analysis. First, we inject the DenseNet201 model implemented on a GPU via NVIDIA’s SASSIFI fault injector. Second, we perform a comprehensive analysis from the perspective of kernel and layer to identify the most vulnerable portions of the injected model. Finally, we validate our technique by applying it to the top-vulnerable kernels to selectively protect the only sensitive portions of the model to avoid unnecessary overheads. Our experiments demonstrate that our mitigation technique achieves a significant reduction in the percentage of errors that cause malfunction (errors that lead to misclassification) from 6.463% to 0.21% . Moreover, the performance overhead (the execution time) of our technique is compared with the well-known protection techniques: Algorithm-Based Fault Tolerance (ABFT), Double Modular Redundancy (DMR), and Triple Modular Redundancy (TMR). The proposed solution shows only 0.3035% overhead compared to these techniques while correcting up 84.8% of the SDC errors in DenseNet201, remarkably improving the healthcare domain’s model reliability

Analyzing the instructions vulnerability of dense convolutional network on GPUS

Recently, Deep Neural Networks (DNNs) have been increasingly deployed in various healthcare applications, which are considered safety-critical applications. Thus, the reliability of these DNN models should be remarkably high, because even a small error in healthcare applications can lead to injury or death. Due to the high computations of the DNN models, DNNs are often executed on the Graphics Processing Units (GPUs). However, the GPUs have been reportedly impacted by soft errors, which are extremely serious issues in the healthcare applications. In this paper, we show how the fault injection can provide a deeper understanding of DenseNet201 model instructions vulnerability on the GPU. Then, we analyze vulnerable instructions of the DenseNet201 on the GPU. Our results show that the most significant vulnerable instructions against soft errors PR, STORE, FADD, FFMA, SETP and LD can be reduced from 4.42% to 0.14% of injected faults, after we applied our mitigation strategy

Rasch Measurement Analysis for Validation Instrument to Evaluate Students Technical Readiness for Embedded Systems

Embedded systems have become a significant manufacturing sector and essential in our life due to their large applications. As a result, higher education institutions acknowledge the significance for offering embedded system design course to electrical, electronics, and computer engineering students. Unfortunately, embedded systems design course continues to be challenging and complex despite current attempts in introducing new embedded system teaching methods. This paper deals with this issue by developing and validating an instrument to measure students' readiness to learn embedded systems using Rasch model. An expert panel was used to verify the content validity and a pilot study (N = 40 respondents) was performed to measure the instrument reliability. A total of 365 respondents from different universities completed the 10-item scale and provided demographic data. The scale dimensionality was evaluated using WINSTEPS 3.92.1, with results showed that all the items fit the Rasch measurement model with acceptable fit index (0.6-1.4) and expressed revealed good consistency, with reliability alpha of 1.00 and 0.72 for items and persons respectively. The instrument was found to have appropriate psychometric properties, and the overall results are well aligned with theoretical expectations. This work has shown that the students were not technically ready for embedded system study

MVDR beamformer model for array response vector mismatch reduction

Beamforrming algorithms attempt to extract a desired User-OF-Interest (UOl) from the background noise and interfering signals. The performance of the beamforming algorithm is evaluated based on various QoS criteria such as beampattern accuracy and signal-to-lnterference-plus-Noise-Ratio (SINR). In this paper, the null-forming constrain is added to the single linear constrain of Minimum Variance Distortionless Response (MVDR) to overcome the effect of finite snapshots problem and the array response vector imprecision. This constraint addition improves the null-forming at the User-Nol-Of InlereKi (UNOI) direction. This work presents a new approach for extract the accurate array response vec-tor. Numerical results show the robustness of the proposed approach to alleviating finite data snapshots effect- Moreover, this technique minimizes the sidelobe level, accurate beam shape lo the UOI direction and pattern null in the UNOIs directions

Real-time Traffic Classification Algorithm Based on Hybrid of Signature Statistical and Port to Identify Internet Applications

Internet traffic classification gained significant attention in the last few years. Most of the current classification methods were only valid for offline classification. The three common classification methods i.e. port, payload and statistics based have some limitations. This paper exploits the advantages of all the three methods by combining them to produce a new classification algorithm called SSPC (Signature Statistical Port Classifier). In the proposed algorithm, each of the three classifiers will individually classify the same traffic flow. Based on certain priority rules, SSPC makes classification decisions for each flow. The SSPC algorithm was used to classifying four types of Internet applications in two stages, initially offline and later online. The results of both cases show that SSPC is the higher accuracy when compared with other classifiers. In addition, as demonstrated in the real time online experiments done, SSPC algorithm uses a short time to classify traffic and thus it is suitable to be used for online classification