FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics

Arefeen, Md Adnan; Nimi, Sumaiya Tabassum; Uddin, Yusuf Sarwar

doi:10.1109/dcoss54816.2022.00033

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…In this conventional setup, there is limited room for improving the latency and/or energy usage of the autonomous system, as the model and the target processing unit is fixed. In response, several studies [2], [3] propose offloading the computation to a remote server, while others [4]- [6] attempt to reduce the computational demand by modifying the underlying model or using a subset of the data stream. However, offloading is not a viable option due to the latency overhead associated with remote processing.…”

Section: Introductionmentioning

confidence: 99%

Multi-accelerator Neural Network Inference in Diversely Heterogeneous Embedded Systems

Dagli

Belviranlı

2021

2021 IEEE/ACM Redefining Scalability for Diversely Heterogeneous Architectures Workshop (RSDHA)

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In recent years, deep neural networks (DNNs) have gained widespread adoption for continuous mobile object detection (OD) tasks, particularly in autonomous systems. However, a prevalent issue in their deployment is the one-size-fits-all approach, where a single DNN is used, resulting in inefficient utilization of computational resources. This inefficiency is particularly detrimental in energy-constrained systems, as it degrades overall system efficiency. We identify that, the contextual information embedded in the input data stream (e.g., the frames in the camera feed that the OD models are run on) could be exploited to allow a more efficient multi-model-based OD process. In this paper, we propose SHIFT which continuously selects from a variety of DNN-based OD models depending on the dynamically changing contextual information and computational constraints. During this selection, SHIFT uniquely considers multi-accelerator execution to better optimize the energy-efficiency while satisfying the latency constraints. Our proposed methodology results in improvements of up to 7.5x in energy usage and 2.8x in latency compared to state-of-the-art GPU-based single model OD approaches.

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