[HTML][HTML] Disclosing edge intelligence: A systematic meta-survey
The Edge Intelligence (EI) paradigm has recently emerged as a promising solution to
overcome the inherent limitations of cloud computing (latency, autonomy, cost, etc.) in the …
overcome the inherent limitations of cloud computing (latency, autonomy, cost, etc.) in the …
[HTML][HTML] A survey on optimization techniques for edge artificial intelligence (ai)
C Surianarayanan, JJ Lawrence, PR Chelliah… - Sensors, 2023 - mdpi.com
Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …
and future business and technical problems. Therefore, AI model engineering processes …
Edge AI for Internet of Medical Things: A literature review
Abstract The Internet of Things (IoT) consists of heterogeneous devices such as wearables
and monitoring devices that collect data to provide autonomous decision-making and smart …
and monitoring devices that collect data to provide autonomous decision-making and smart …
GreedyGD: Enhanced generalized deduplication for direct analytics in IoT
The exponential growth of data generated by the Internet of Things presents significant
challenges for data communication, storage, and analytics. Consequently, organizations …
challenges for data communication, storage, and analytics. Consequently, organizations …
A comparative study on cloud and edgeb computing: A survey on current research activities and applications
Cloud computing and edge computing are distinct architectures that process data. Cloud
computing is a centralized model of computing where data is stored and processed in a …
computing is a centralized model of computing where data is stored and processed in a …
GLEAN: Generalized-deduplication-enabled approximate edge analytics
The Internet of Things (IoT) has brought about exponential growth in sensor data. This has
led to increasing demands for efficient and novel data transmission, storage, and analytics …
led to increasing demands for efficient and novel data transmission, storage, and analytics …
Partitioning and placement of deep neural networks on distributed edge devices to maximize inference throughput
A Parthasarathy… - 2022 32nd International …, 2022 - ieeexplore.ieee.org
Edge inference has become more widespread, as its diverse applications range from retail
to wearable technology. Clusters of networked resource-constrained edge devices are …
to wearable technology. Clusters of networked resource-constrained edge devices are …
Partitioning and deployment of deep neural networks on edge clusters
A Parthasarathy, B Krishnamachari - arXiv preprint arXiv:2304.11941, 2023 - arxiv.org
Edge inference has become more widespread, as its diverse applications range from retail
to wearable technology. Clusters of networked resource-constrained edge devices are …
to wearable technology. Clusters of networked resource-constrained edge devices are …
Technique of Feature Extraction Based on Interpretation Analysis for Multilabel Learning in Nonintrusive Load Monitoring With Multiappliance Circumstances
Nonintrusive load monitoring (NILM) aims to analyze the aggregate information of power
consumption and recognize the separate operation states of each individual electrical …
consumption and recognize the separate operation states of each individual electrical …
PairwiseHist: Fast, Accurate and Space-Efficient Approximate Query Processing with Data Compression
Exponential growth in data collection is creating significant challenges for data storage and
analytics latency. Approximate Query Processing (AQP) has long been touted as a solution …
analytics latency. Approximate Query Processing (AQP) has long been touted as a solution …