The recent shift in machine learning towards the edge offers a new opportunity to realize intelligent applications on resource constrained Internet of Things (IoT) hardware. This paper presents a pre-trained Recurrent Neural Network (RNN) model optimized for an IoT device running on 8-bit microcontrollers. The device is used for data acquisition in a research on the impact of prolonged sedentary work on health. Our prediction model facilitates smart data transfer operations to reduce the energy consumption of the device. Application specific optimizations were applied to deploy and execute the pre-trained model on a device which has only 8 KB RAM size. Experiments show that the resulting edge intelligence can reduce the communication cost significantly, achieving subs-tantial saving in energy used by the IoT device.
|Status||Publicerad - 2019|
|Evenemang||SPWID 2019: The Fifth International Conference on Smart Portable, Wearable, Implantable and Disabilityoriented Devices and Systems - |
Varaktighet: 1980-jan.-01 → …
|Konferens||SPWID 2019: The Fifth International Conference on Smart Portable, Wearable, Implantable and Disabilityoriented Devices and Systems|
|Period||80-01-01 → …|
- Data- och informationsvetenskap (102)