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arXiv · 2108.12914

Leveraging Transprecision Computing for Machine Vision Applications at the Edge

Umar Ibrahim Minhas·Lev Mukhanov·Georgios Karakonstantis·Hans Vandierendonck·Roger Woods
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Abstract

Machine vision tasks present challenges for resource constrained edge devices, particularly as they execute multiple tasks with variable workloads. A robust approach that can dynamically adapt in runtime while maintaining the maximum quality of service (QoS) within resource constraints, is needed. The paper presents a lightweight approach that monitors the runtime workload constraint and leverages accuracy-throughput trade-off. Optimisation techniques are included which find the configurations for each task for optimal accuracy, energy and memory and manages transparent switching between configurations. For an accuracy drop of 1%, we show a 1.6x higher achieved frame processing rate with further improvements possible at lower accuracy.

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Umar Ibrahim Minhas, Lev Mukhanov, Georgios Karakonstantis, Hans Vandierendonck, Roger Woods. 2021-08-29. Leveraging Transprecision Computing for Machine Vision Applications at the Edge. https://arxiv.org/abs/2108.12914

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