Together with design partner Edge Impulse, Nordic Semiconductor has introduced TinyML support for both the nRF9160 DK (development kit) and the Nordic Thingy:91 multi-sensor cellular IoT prototyping platform. Following the introduction, both Nordic low-power cellular IoT development tools are integrated into Edge Impulse’s ‘Edge Impulse Studio’.
The collaboration allows developers without TinyML programming expertise to quickly get started on fully supported, standalone ‘inferencing’ (applied machine learning (ML) at the device level) example projects based on Nordic’s nRF Connect SDK (software development kit). The example projects can then be run on the nRF9160 DK and Thingy:91.
TinyML is a scaled-down form of ML suitable for Internet of Things (IoT) edge devices such as wireless sensors. When using TinyML, cloud-based ML training and model synthesis is made simpler and faster compared with conventional techniques that rely on server training of increasingly large and complex models. The resultant lightweight models can then run efficiently on the optimised compute resources of the target edge devices.
nRF9160 SiP-based solution
The nRF9160 DK and the Thingy:91 are based on the Nordic nRF9160 low-power System-in-Package (SiP) with integrated LTE-M/NB-IoT modem and GPS and are used for the prototyping and development of low-power cellular IoT products. Edge Impulse Studio is a development platform used to collect data from sensors, train a TinyML model and then deploy that model to the target device.
The nRF9160 SiP is certified for global cellular IoT applications and enables IoT data to be sent over distances of kilometres. In addition to a multimode LTE-M/NB-IoT modem, 1 MB Flash and 256 KB RAM, the nRF9160 features a dedicated Arm Cortex-M33 applications processor – making it an ideal edge device for supporting TinyML applications.
Fast route to TinyML
All that’s needed for developers to get started with TinyML projects is access to Edge Impulse Studio, an nRF9160 DK and some sensors, or a Thingy:91 (which incorporates built-in sensors for motion, impact, air quality and more). Once the nRF9160 development tools are linked to Edge Impulse Studio it is straightforward to build and deploy a ready-to-go binary file of an industrial-grade TinyML algorithm for a low-power cellular IoT application.
To help developers get started with TinyML projects on the nRF91 Series, Edge Impulse has published a couple of blogs with links to tutorials for nRF9160 DK and Thingy:91 (www.edgeimpulse.com).
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