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1. Feature Compression for Rate Constrained Object Detection on the Edge

   Yuan, Zhongzheng; Samyak Rawlekar; Siddharth Garg; Elza Erkip; Yao Wang (August 2022, In MLSys 2023 Workshop on Resource-Constrained Learning in Wireless Networks)

   Recent advances in computer vision has led to a growth of interest in deploying visual analytics model on mobile devices. However, most mobile devices have limited computing power, which prohibits them from running large scale visual analytics neural networks. An emerging approach to solve this problem is to offload the computation of these neural networks to computing resources at an edge server. Efficient computation offloading requires optimizing the trade-off between multiple objectives including compressed data rate, analytics performance, and computation speed. In this work, we consider a “split computation” system to offload a part of the computation of the YOLO object detection model. We propose a learnable feature compression approach to compress the intermediate YOLO features with lightweight computation. We train the feature compression and decompression module together with the YOLO model to optimize the object detection accuracy under a rate constraint. Compared to baseline methods that apply either standard image compression or learned image compression at the mobile and perform image de-compression and YOLO at the edge, the proposed system achieves higher detection accuracy at the low to medium rate range. Furthermore, the proposed system requires substantially lower computation time on the mobile device with CPU only. 

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2. Joint Compression and Offloading Decisions for Deep Learning Services in 3-Tier Edge Systems

   https://doi.org/10.1109/DySPAN53946.2021.9677398  

   Hosseinzadeh, Minoo; Hudson, Nathaniel; Zhao, Xiaobo; Khamfroush, Hana; Lucani, Daniel E. (December 2021, 2021 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN))

   Task offloading in edge computing infrastructure remains a challenge for dynamic and complex environments, such as Industrial Internet-of-Things. The hardware resource constraints of edge servers must be explicitly considered to ensure that system resources are not overloaded. Many works have studied task offloading while focusing primarily on ensuring system resilience. However, in the face of deep learning-based services, model performance with respect to loss/accuracy must also be considered. Deep learning services with different implementations may provide varying amounts of loss/accuracy while also being more complex to run inference on. That said, communication latency can be reduced to improve overall Quality-of-Service by employing compression techniques. However, such techniques can also have the side-effect of reducing the loss/accuracy provided by deep learning-based service. As such, this work studies a joint optimization problem for task offloading decisions in 3-tier edge computing platforms where decisions regarding task offloading are made in tandem with compression decisions. The objective is to optimally offload requests with compression such that the trade-off between latency-accuracy is not greatly jeopardized. We cast this problem as a mixed integer nonlinear program. Due to its nonlinear nature, we then decompose it into separate subproblems for offloading and compression. An efficient algorithm is proposed to solve the problem. Empirically, we show that our algorithm attains roughly a 0.958-approximation of the optimal solution provided by a block coordinate descent method for solving the two sub-problems back-to-back. 

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3. Progressive Neural Compression for Adaptive Image Offloading Under Timing Constraints

   https://doi.org/10.1109/RTSS59052.2023.00020  

   Wang, Ruiqi; Liu, Hanyang; Qiu, Jiaming; Xu, Moran; Guérin, Roch; Lu, Chenyang (December 2023, IEEE)

   IoT devices are increasingly the source of data for machine learning (ML) applications running on edge servers. Data transmissions from devices to servers are often over local wireless networks whose bandwidth is not just limited but, more importantly, variable. Furthermore, in cyber-physical systems interacting with the physical environment, image offloading is also commonly subject to timing constraints. It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices. In this paper, we use image classification as our target application and propose progressive neural compression (PNC) as an efficient solution to this problem. Although neural compression has been used to compress images for different ML applications, existing solutions often produce fixed-size outputs that are unsuitable for timing-constrained offloading over variable bandwidth. To address this limitation, we train a multi-objective rateless autoencoder that optimizes for multiple compression rates via stochastic taildrop to create a compression solution that produces features ordered according to their importance to inference performance. Features are then transmitted in that order based on available bandwidth, with classification ultimately performed using the (sub)set of features received by the deadline. We demonstrate the benefits of PNC over state-of-the-art neural compression approaches and traditional compression methods on a testbed comprising an IoT device and an edge server connected over a wireless network with varying bandwidth. 

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4. Scalable Feature Compression for Edge-Assisted Object Detection Over Time-Varying Networks

   Yuan, Zhongzheng (May 2023, In MLSys Workshop on Resource-Constrained Learning in Wireless Networks)

   Split-computing has recently emerged as a paradigm for offloading computation of visual analytics models from low-powered mobile devices to edge or cloud servers, by which the mobiles execute part of the model and compress and send the intermediate features, and the servers complete the remaining model computation. Prior feature compression approaches train different compression models and possibly visual analytics models to reach different target bit rates. We propose a scalable compression model that compresses the intermediate features of the YOLO object detection model into a layered bitstream, which can be easily adapted to meet the rate constraint of a dynamic network. Our approach achieves comparable rate-accuracy performance compared to prior non-scalable compression approaches over a large bitrate range. 

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5. Adaptive Deep Learning for Soft Real-Time Image Classification

   https://doi.org/https://doi.org/10.3390/technologies9010020  

   Chai, F; Kang, K. D. (March 2021, Technologies)

   null (Ed.)

   CNNs (Convolutional Neural Networks) are becoming increasingly important for real-time applications, such as image classification in traffic control, visual surveillance, and smart manufacturing. It is challenging, however, to meet timing constraints of image processing tasks using CNNs due to their complexity. Performing dynamic trade-offs between the inference accuracy and time for image data analysis in CNNs is challenging too, since we observe that more complex CNNs that take longer to run even lead to lower accuracy in many cases by evaluating hundreds of CNN models in terms of time and accuracy using two popular data sets, MNIST and CIFAR-10. To address these challenges, we propose a new approach that (1) generates CNN models and analyzes their average inference time and accuracy for image classification, (2) stores a small subset of the CNNs with monotonic time and accuracy relationships offline, and (3) efficiently selects an effective CNN expected to support the highest possible accuracy among the stored CNNs subject to the remaining time to the deadline at run time. In our extensive evaluation, we verify that the CNNs derived by our approach are more flexible and cost-efficient than two baseline approaches. We verify that our approach can effectively build a compact set of CNNs and efficiently support systematic time vs. accuracy trade-offs, if necessary, to meet the user-specified timing and accuracy requirements. Moreover, the overhead of our approach is little/acceptable in terms of latency and memory consumption. 

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