  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.
