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| 16. |
- Sánchez Leal, Isaac, et al.
(författare)
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Impact of input data on intelligence partitioning decisions for IoT smart camera nodes
- 2021
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Ingår i: Electronics. - : MDPI AG. - 2079-9292. ; 10:16
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Tidskriftsartikel (refereegranskat)abstract
- Image processing systems exploit image information for a purpose determined by the application at hand. The implementation of image processing systems in an Internet of Things (IoT) context is a challenge due to the amount of data in an image processing system, which affects the three main node constraints: memory, latency and energy. One method to address these challenges is the partitioning of tasks between the IoT node and a server. In this work, we present an in-depth analysis of how the input image size and its content within the conventional image processing systems affect the decision on where tasks should be implemented, with respect to node energy and latency. We focus on explaining how the characteristics of the image are transferred through the system until finally influencing partition decisions. Our results show that the image size affects significantly the efficiency of the node offloading configurations. This is mainly due to the dominant cost of communication over processing as the image size increases. Furthermore, we observed that image content has limited effects in the node offloading analysis.
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| 17. |
- Sánchez Leal, Isaac, et al.
(författare)
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Waist Tightening of CNNs : A Case study on Tiny YOLOv3 for Distributed IoT Implementations
- 2023
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Ingår i: ACM International Conference Proceeding Series. - : Association for Computing Machinery (ACM). - 9798400700491 ; , s. 241-246
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Konferensbidrag (refereegranskat)abstract
- Computer vision systems in sensor nodes of the Internet of Things (IoT) based on Deep Learning (DL) are demanding because the DL models are memory and computation hungry while the nodes often come with tight constraints on energy, latency, and memory. Consequently, work has been done to reduce the model size or distribute part of the work to other nodes. However, then the question arises how these approaches impact the energy consumption at the node and the inference time of the system. In this work, we perform a case study to explore the impact of partitioning a Convolutional Neural Network (CNN) such that one part is implemented on the IoT node, while the rest is implemented on an edge device. The goal is to explore how the choice of partition point, quantization method and communication technology affects the IoT system. We identify possible partitioning points between layers, where we transform the feature maps passed between layers by applying quantization and compression to reduce the data sent over the communication channel between the two partitions in Tiny YOLOv3. The results show that a reduction of transmitted data by 99.8% reduces the network accuracy by 3 percentage points. Furthermore, the evaluation of various IoT communication protocols shows that the quantization of data facilitates CNN network partitioning with significant reduction of overall latency and node energy consumption.
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| 18. |
- Saqib, Eiraj, et al.
(författare)
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Optimizing the IoT Performance : A Case Study on Pruning a Distributed CNN
- 2023
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Ingår i: 2023 IEEE Sensors Applications Symposium (SAS). - 9798350323078
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Konferensbidrag (refereegranskat)abstract
- Implementing Convolutional Neural Networks (CNN) based computer vision algorithms in Internet of Things (IoT) sensor nodes can be difficult due to strict computational, memory, and latency constraints. To address these challenges, researchers have utilized techniques such as quantization, pruning, and model partitioning. Partitioning the CNN reduces the computational burden on an individual node, but the overall system computational load remains constant. Additionally, communication energy is also incurred. To understand the effect of partitioning and pruning on energy and latency, we conducted a case study using a feet detection application realized with Tiny Yolo-v3 on a 12th Gen Intel CPU with NVIDIA GeForce RTX 3090 GPU. After partitioning the CNN between the sequential layers, we apply quantization, pruning, and compression and study the effects on energy and latency. We analyze the extent to which computational tasks, data, and latency can be reduced while maintaining a high level of accuracy. After achieving this reduction, we offloaded the remaining partitioned model to the edge node. We found that over 90% computation reduction and over 99% data transmission reduction are possible while maintaining mean average precision above 95%. This results in up to 17x energy savings and up to 5.2x performance speed-up.
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| 19. |
- Shallari, Irida, et al.
(författare)
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Design space exploration for an IoT node : Trade-offs in processing and communication
- 2021
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Ingår i: IEEE Access. - 2169-3536. ; 9, s. 65078-65090
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Tidskriftsartikel (refereegranskat)abstract
- Optimising the energy consumption of IoT nodes can be tedious due to the due to complex trade-offs involved between processing and communication. In this article, we investigate the partitioning of processing between the sensor node and a server and study the energy trade-offs involved. We propose a method that provides a trade-off analysis for a given set of constraints and allows for exploring several intelligence partitioning configurations. Furthermore, we demonstrate how this method can be used for the analysis of four design examples with traditional and CNN-based image processing systems, and we also provide an implementation of it on Matlab. CCBY
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