New Product Introduction

Nordic Semiconductor Nordic Thingy:53

Nordic Thingy:53 - Multi-sensor prototyping platform for wireless IoT and embedded machine learning

Nordic Thingy:53 - top side of the PCB and the board

The Nordic Thingy:53 is a rapid prototyping platform based on the nRF5340 System-on-Chip (SoC), our current flagship dual-core wireless SoC. With integrated sensors for motion, sound, light and environmental factors, it is the perfect platform for building proofs-of-concept and developing new prototypes in a very short time. The Thingy:53 supports a wide range of wireless protocols, including Bluetooth LE, Bluetooth mesh, Thread, Zigbee, NFC, and the new smart-home standard Matter. It also draws the benefit of using our new nRF21540 RF front-end module, which allows for better range and increased link robustness. Power management is handled by the nPM1100 PMIC, which has full power path functionality, meaning that it seamlessly transitions between charging the battery and being powered by the battery when the external source is disconnected.

The Thingy:53 comes pre-installed with firmware to work with the nRF Edge Impulse mobile app to create and run embedded machine learning applications directly on the Thingy:53 with Edge Impulse Studio. This enables you to take full advantage of the advanced sensors of the Thingy:53 in applications like voice recognition or movement pattern detection. The low-power accelerometer and the PDM microphone can also wake the SoC from sleep on motion or sound events. This is especially useful for creating low-power embedded machine learning applications, allowing the device to remain sleeping and save power when there is nothing to register or react to.

The Arm Cortex-M33 application processor core of the nRF5340 SoC ensures that the Thingy:53 can handle heavy computational tasks of embedded machine learning without affecting the wireless connectivity. The application core is clocked at 128 MHz for the best possible performance, with ample room for your applications on its 1 MB of flash storage, and 512 KB RAM. Wireless connectivity is handled separately by another Arm Cortex-M33 core clocked at a lower 64 MHz for more power efficient operation, for continuous wireless connection without taking up any computational resources from the application core.

 

nRF Edge Impulse Machine Learning App

The Nordic Thingy:53 comes pre-installed with the nRF Edge Impulse app firmware, designed to enable embedded machine learning directly on the Thingy:53. The app allows for integration with Edge Impulse Studio to create and run embedded machine learning applications on the Thingy:53, like voice command recognition or recognizing different movement patterns.

 

Key features

  • Battery powered prototyping platform for the nRF5340 SoC
  • Support for multiple wireless standards - Bluetooth LE, Bluetooth mesh, Thread, Zigbee
  • Ideal for proof-of-concept Matter development
  • nRF Edge Impulse mobile app for embedded machine learning
  • nRF Programmer mobile app for easily flashing firmware on the go
  • User-programmable button and RGB LEDs
  • Environmental sensor for temperature, humidity, air quality and air pressure
  • Color and light sensor
  • Low-power accelerometer and 6-axis inertial measurement unit (IMU)
  • Buzzer and PDM microphone
  • Connector for additional external boards and accessories
  • External debug and current measurement board
  • USB-C rechargeable 1350 mAh Li-Po battery

 

nRF5340 SoC

  • High performance 128 MHz Arm Cortex-M33 application processor
    • 1 MB Flash and 512 KB RAM for application firmware
  • Ultra-low power 64 MHz Arm Cortex-M33 network processor
    • 256 KB Flash and 64 KB RAM for protocol stack firmware
  • Multiprotocol radio with support for Bluetooth LE, Thread, Zigbee, Bluetooth mesh and proprietary 2.4 GHz protocols

 

nPM1100 PMIC

  • Highly efficient PMIC for improved battery life
  • Full power path for seamless switching between charging and battery operation

 

nRF21540 FEM

  • RF front end for extended range
  • Increased link robustness

 

Applications

  • Machine learning
  • Smart home sensing
  • Fast prototyping
  • Proof-of-concept development

 

Related Documents



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