Edge AI: Addressing the Efficiency Paradigm

Bailey, Colleen P.; Depoian, Arthur C.; Adams, Ethan

doi:10.1109/metrocon56047.2022.9971140

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…The surge in data in both size and volume exacerbates the need for efficient solutions, particularly in real-time applications with stringent latency requirements. 4 Efficiency in compression schemes is even more paramount in many applications. In applications with limited resources, including bandwidth or storage, highly efficient compression schemes become essential to ensure fast processing and minimal delays.…”

Section: Introductionmentioning

confidence: 99%

Practical real-time image compression for resource-challenged devices

Pham,

Depoian,

Bailey

2024

Multimodal Image Exploitation and Learning 2024

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In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional image processing tasks. However, many of the existing solutions in this domain are burdened by computational complexity, rendering them unsuitable for real-time deployment on standard devices as they often necessitate complex systems and substantial energy consumption.This work addresses the growing paradigm of edge computing for real-time applications by introducing a novel, on-edge device solution. This innovative approach aims to strike a balance between efficiency and accuracy, adhering to the practical constraints of real-world deployment. By presenting demonstrations of the proposed solution's performance on readily available devices, we provide tangible evidence of its applicability and viability in real-world scenarios. This advance contributes to the ongoing dialogue about the need for accessible and efficient image compression algorithms that can be deployed real-time applications on edge devices, bridging the gap between the demanding computational requirements of deep learning and the practical limitations of everyday hardware.As data continues to surge, solutions like this become ever more critical in ensuring effective image compression, aligning with on-edge computing within AI. This research paves the way for improved image processing in real-time applications while conserving computational resources and energy consumption.

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Section: Introductionmentioning

confidence: 99%

Practical real-time image compression for resource-challenged devices

Pham,

Depoian,

Bailey

2024

Multimodal Image Exploitation and Learning 2024

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“…Efficient machine learning is crucial for achieving real-time processing capabilities, especially in the context of airport security screening. 5,6 With the ever-increasing volume of passengers and luggage, there is a growing demand for systems that can quickly and accurately identify potential threats without causing undue delays or disruptions. The ability to process data in real-time allows security personnel to respond promptly to potential threats, enhancing overall security levels.…”

Section: Introductionmentioning

confidence: 99%

Improving real-time security screening

Hand,

Bailey

2024

Real-Time Image Processing and Deep Learning 2024

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The Transportation Security Administration (TSA) is responsible for air travel safety within the United States but faces a significant challenge. Recent studies show an alarming 80% failure rate in threat detection at most screening locations, primarily due to heavy reliance on human judgment. With more than 50,000 TSA officers screening over 2 million passengers daily, it is essential to address this issue promptly, as evidenced by a 42% increase in complaints related to TSA screening over the past year, according to the US Department of Transportation's monthly air travel consumer report. These complaints underscore the pressing need for improved threat detection procedures in airport security.In response to these critical concerns, we present a novel and efficient neural network classification algorithm as a potential solution, specifically designed to mitigate the identified shortcomings in the TSA's threat detection capabilities. By reducing the overall complexity of larger models, through the application of advanced layers and an artfully configured structure, we achieve a solution that maximizes efficiency without compromising accuracy. This research bridges the gap between the demands of contemporary threat detection and the practical limitations of airport security procedures. By introducing a tailored solution, we aim to significantly enhance the efficacy of threat detection, thereby contributing to the overall safety and security of air travel. This work represents a pivotal step in addressing the critical issues associated with the TSA's current screening methods and underscores the potential of advanced technology to bolster the reliability of threat detection systems.

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“…It is also important to consider the efficiency of machine learning networks. 5 Efficiency improvements reduce computational intensity of machine learning algorithms, therefore reducing consumption of energy. This will reduce the cost of utilizing neural networks, making them more viable for widespread use.…”

Section: Introductionmentioning

confidence: 99%

Augmented multi-head classification network: MHATT

Cayce

Depoian

Bailey

et al. 2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

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Classification of one-dimensional (1D) data is important for a variety of complex problems. From the finance industry to audio processing to the medical field, there are many industries that utilize 1D data. Machine learning techniques have excelled at solving these classification problems, but there is still room for improvement because the techniques have not been perfected. This paper proposes a novel architecture called Multi-Head Augmented Temporal Transformer (MHATT) for 1D classification of time-series data. Highly modified vision transformers were used to improve performance while keeping the network exceptionally efficient. To showcase its efficacy, the network is applied to heartbeat classification using the MIT-BIH OSCAR dataset. This dataset was ethically-split to ensure a fair and intensive test for networks. The novel architecture is 94.6% more efficient and had a peak accuracy of 91.79%, which was a 13.6% reduction in error over a recent state-of-the-art network. The impressive performance and efficiency of the MHATT architecture can be exploited by edge devices for unmatched performance and flexibility of deployment.

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Edge AI: Addressing the Efficiency Paradigm

Cited by 9 publications

References 13 publications

Practical real-time image compression for resource-challenged devices

Practical real-time image compression for resource-challenged devices

Improving real-time security screening

Augmented multi-head classification network: MHATT

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