Quicklogic taps European developer for low power machine learning board

Quicklogic taps European developer for low power machine learning board

Business news |
By Nick Flaherty

QuickLogic has teamed with European developer Antmicro on a low power board for edge computing and machine learning systems. The QuickFeather board (above) is aimed at low power Machine Learning (ML) capable IoT endpoint devices.

On top of the open source hardware design, now available on GitHub, Antmicro, based in Sweden and Poland, 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 uses QuickLogic’s EOS S3 FPGA which combines eFPGA logic integrated with an Arm Cortex-M4F MCU and is the first SoC to be fully supported in the Zephyr RTOS. The board includes 16Mbit of flash memory, an MC3635 accelerometer, Infineon DPS310 pressure sensor and Infineon IM69D130 PDM digital microphone and can be powered from USB or a single Li-Po battery with an integrated battery charger.

The QuickFeather development board was created to allow developers to explore the EOS S3 platform through  compatibility with extensions available for the Feather PCB format from Adafruit, recently also added as a Zephyr Project member. The EOS S3 is supported in Antmicro’s Renode open source simulation framework for rapid prototyping, development and testing of multi-node systems, offering a more efficient hardware/software co-design approach. 

“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,” said Michael Gielda, vice president of business development at Antmicro. “We are proud to be helping QuickLogic build an open hardware and software ecosystem that can serve as a model for the entire industry.”

“Machine learning applications are being deployed at an amazing rate and the new QuickFeather board will further accelerate that trend,” said 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.”

The QuickFeather board with integrated EOS S3 voice and sensor processing SoC, and the Renode simulation environment are all sampling now and will be available in early Q2.

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