Current mainstream approach to sensor data monitoring usually relies on cloud access: samples are acquired by connected devices and data processing is performed on remote servers. To improve responsiveness, security and resilience, devices and programming methodologies must be improved, with the aim of enabling data analytics at the edge. Unfortunately this is not an easy task, especially in the IoT domain. In this paper, we present a research approach that manages at runtime the hardware/software configuration of a low-power processing system, with the aim of adapting to dynamically changing workloads optimizing power-relevant settings to the corresponding operating point. First, we present a first validation experiment, involving a hardware-software architecture for a connected sensor-processing node that allows the set of in-place processing tasks to be executed to be remotely controllable by an external user. The designed system is capable of dynamically adapting its operating point to the selected computational load, to minimize power consumption. The benefits of the proposed approach are tested on a use-case involving ECG monitoring, that, when selected, performs ECG classification using a lightweight convolutional neural network. Experimental results show how the proposed approach can provide more than 50% power consumption reduction for common ECG activity, with less than 2% memory footprint overhead and reconfiguring the system in less than 1 ms. Second we present our plans to extend this approach to more complex multi-core systems.
Runtime-adaptive cognitive IoT nodes
Loi D.;Raffo L.;Meloni P.
2019-01-01
Abstract
Current mainstream approach to sensor data monitoring usually relies on cloud access: samples are acquired by connected devices and data processing is performed on remote servers. To improve responsiveness, security and resilience, devices and programming methodologies must be improved, with the aim of enabling data analytics at the edge. Unfortunately this is not an easy task, especially in the IoT domain. In this paper, we present a research approach that manages at runtime the hardware/software configuration of a low-power processing system, with the aim of adapting to dynamically changing workloads optimizing power-relevant settings to the corresponding operating point. First, we present a first validation experiment, involving a hardware-software architecture for a connected sensor-processing node that allows the set of in-place processing tasks to be executed to be remotely controllable by an external user. The designed system is capable of dynamically adapting its operating point to the selected computational load, to minimize power consumption. The benefits of the proposed approach are tested on a use-case involving ECG monitoring, that, when selected, performs ECG classification using a lightweight convolutional neural network. Experimental results show how the proposed approach can provide more than 50% power consumption reduction for common ECG activity, with less than 2% memory footprint overhead and reconfiguring the system in less than 1 ms. Second we present our plans to extend this approach to more complex multi-core systems.
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