Small dev board enables low-power ML in IoT devices

March 09, 2020 //By Rich Pell
IoT dev board brings low-power ML to endpoint devices
Chipmaker QuickLogic Corporation and embedded technology company Antmicro have announced a small-form-factor development board designed to enable the next generation of low-power machine learning (ML) capable IoT devices.

Based on open source hardware, the QuickFeather Development Kit is compatible with the Adafruit Feather form factor, and is built around 100% open source software (including the Symbiflow FPGA Tools). The board, say the companies, is designed to simplify the implementation of ML algorithms on endpoint IoT devices.

On top of the open source hardware design, available on GitHub today, Antmicro has also added support for the QuickFeather board in the Zephyr Real Time Operating System (RTOS) as well as in its open source Renode simulation framework. The QuickFeather board is powered by QuickLogic's EOS S3 FPGA-enabled SoC, with flexible eFPGA logic integrated with an Arm Cortex-M4F MCU and functionality including the following:

  • 16-Mbit of flash memory
  • MC3635 accelerometer
  • Infineon DPS310 pressure sensor
  • Infineon IM69D130 PDM digital microphone
  • User button and RGB LED
  • Powered from USB or a single Li-Po battery
  • Integrated battery charger
  • USB data signals tied to programmable logic

"Machine learning applications are being deployed at an amazing rate and the new QuickFeather board will further accelerate that trend," says Brian Faith, president and chief executive officer of QuickLogic. "Developers love the fact that it and its associated Renode simulation framework are open source, making it even more attractive for implementing ML algorithms on endpoint IoT applications."

Michael Gielda, vice president of business development at Antmicro says, "An open hardware development board for a cost effective, FPGA-enabled SoC platform coupled with useful sensors, supported in a mainstream open source RTOS and the open source Renode simulation framework, QuickFeather is ideally positioned for use in tiny ML applications such as SensiML's AI Software Platform and Google's TensorFlow Lite. We are proud to be helping QuickLogic build an open hardware and software ecosystem that can serve as a model for the entire industry."

The QuickFeather board and associated software is sampling now and will be available in early Q2.

QuickLogic
Antmicro


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