Vision FPGA SoM


An FPGA-based SoM with integrated vision, audio, and motion-sensing capability

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of $6,300 goal

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Vision FPGA SoM

A FPGA Vision SoM to play with!


SoM Breakout Board

The SoM breakout board brings all SoM pins to 2.54 mm pitch pins for breadboarding, can't do without it! SoMe assembly required to solder the 2.54 mm headers on. Note: SoM is not included.


SoM Developer Board

One developer board that breaks out SoM IO with in-line LEDs, includes a Raspberry Pi HAT connector, allows for USB power (with power monitoring) and programming, includes PMOD and QWIIC connectors, and has room for prototyping. Note: SoM is NOT included.


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Low-power Computer Vision

Embedded Computer Vision is a highly multi-disciplinary field that requires expertise in optics, image sensors, hardware, firmware and so on. As a result, the bar to playing with technology in this field is quite high.

The Vision FPGA SoM is a Lattice FPGA based System on Module that integrates an ultra-low-power vision sensor, a 3-axis accelerometer/gyroscope, and an I²S MEMS microphone in a small form-factor (3 cm x 2 cm).

This device is built for developers who want to not only play with this technology, but also have a path to integrating it into real world products. We designed the device with the following thoughts in mind:

  • Low Power Image Sensor + FPGA: Overall power consumption is minimized and in the 10-20 mW range to enable battery powered applications.
  • Well-designed Host Interface: Easy to integrate into a larger system using an interrupt driven SPI host interface, programmable IO voltages to support a glueless HW interface.
  • Modular Design: Complexity will be localized to the SoM so that the developer can quickly integrate this device into their system using a breadboard as well as a simple API. All required components for vision/audio/motion (eg. image sensor and illumination) will be integrated so the developer doesn't have to cobble together parts. The solution translates into a final product with minimal changes.
  • Flexible and Easy to Use: Easy to integrate mechanically and electrically. The SoM will appear like an SPI device with a SW library to support it. The user has the ability to configure the FPGA CRAM instead of having to program the flash followed by a reset to configure the FPGA. The developer has the flexibility to choosing an image sensor while providing a reasonable, low-cost default option.
  • Strong Set of Features: Vision FPGA SoM is not just an FPGA board. Require a reasonably sized SRAM to allow for temporary data storage at low power (not DRAM!) which is especially important for vision applications where multiple image frames may need to be captured/processed. Audio and IMU are commonly used together with vision and will make sense to integrate on this platform.
  • Documented and Open FPGA Code: The SoM supports an open source toolchain. Well-documented code and plenty of simple examples for each subsystem exists, as well as a large design that ties together various parts of the SoM.
  • Production Capable: The SoM form-factor and connectorized interface enable a smooth transition from prototype to production.

The modular concept is summarized in a talk given at a tinyML meetup in 2019.

Flexible and Tightly Integrated

By including most commonly used sensors in a configurable platform utilizing an FPGA with an open source toolchain, this device enables developers to experiment and build quickly. Sample FPGA, as well as host code will be provided as a jumping off point for backers.

Features & Specifications

  • The main processing element is the Lattice iCE40UP5k FPGA
    • 5K LUT's
    • 1 Mb RAM
    • 8 MAC units
  • Main Onboard Sensor:
    • Color, rolling shutter imager (Himax HM01B0)
  • Other Onboard Sensors:
    • Knowles MEMS I²S microphone, expandable to a stereo configuration with an off-board I²S microphone
    • InvenSense IMU 60289 6-axis Gyro/accelerometer
  • Memory:
    • 4 Mb qSPI Flash for FPGA bitstream/code storage
    • 64 Mb qSPI SRAM for temporary data
  • LED's:
    • Tri-colour LED for a user interface driven by the FPGA
    • IR LED for low-light illumination with frame exposure synchronization
  • GPIO:
    • Four GPIO, each with programmable IO voltage
    • Four-wire SPI host interface with programmable IO voltage
  • Flexible Power Options:
    • Single 3.3 V operation, can supply 1.8 V and 1.2 V at 100 mA (max) to external devices using onboard LDO's
    • External 3.3 V, 1.8 V, 1.2 V for lower power operation
  • Neural Network Support: Supports the Lattice SensAI toolchain using Tensorflow/Caffe/Keras for model development, quantization, and mapping to the SensAI Neural Network engines. Inference at >30 FPS at ~25 mW (average). Sample FPGA code provided for:
    • Image capture
    • Vision-based people detection
    • Audio keyword detection
  • Small size: 2 cm x 3 cm

Accessories for Development and Prototyping

SoM Developer Board

The Developer Board provides the following functionality that enables application development using the module:

  • Brings out all IO to headers
  • Micro USB based power and FPGA programming
  • LED's on all GPIO for signal monitoring
  • Module power monitoring to allow for application development
  • Standard headers (dual Pmod and Qwiic) for attaching expansion boards such as Wi-Fi/BLE and other sensors for quick prototyping
  • Small prototyping area
  • Support for up to 4 Servo's using PWM control (power must be provided externally!) under FPGA control

The Developer board also has a Raspberry Pi Hat connector. This leverages the extensive RPi ecosystem to develop applications using the Vision FPGA SoM. This enables new use cases for a Raspberry Pi Zero W (including the ones as shown in Adafruit Pi Hat:

  • Image Sensing: The RPi can capture images (qVGA or 320x240) without adding the high resolution camera (Normally $29.95 or so).
  • Microphone Capability: The Pi can use the MEMS microphone on the SoM over the I2S port, enabling you RPi to listen!
  • Accelerometer/Gyroscope: The Pi can talk directly to the IMU on the SoM enabling motion sensing on the Pi.
  • AI acceleration: The RPi, while being quite capable in terms of running Linux, isn't as good at running Neural Networks as the FPGA on the SoM. The RPi hat option enables the RPi and Vision SoM to take advantage of the strengths of each device.

SoM Breakout Board

The breakout board is in the Adafruit Feather form factor and has the following features:

  • All pins of the Vision SoM are brought out to 2.54mm breadboard compatible headers
  • Micro USB charging, Li poly charger circuit with automatic switchover
  • QWIIC connector to allow interfacing with the QWIIC ecosystem of sensors, displays etc.

Note: the FPGA programming must be done using an external SPI master. The USB port is purely to supply power.


ItemFPGA Vision SoMiCEBreakerTinyFPGA BXTomu FPGA
Price $100$69$38$45$49.49
Schematics Published? YesYesYesNot yetYes
Design Files Published? Not YetYesYesNot yetNo
Volume Production Friendly? YesNoNoNoNo
Logic Capacity (LUTs) 52805280768052801280
Internal RAM (bits) 120k + 1024k120k + 1024k128k120k + 1024k64k
Multipliers 88080
USB Interface FTDI 2232HQ (dev kit)FTDI 2232HQOn FPGA bootloaderOn FPGA bootloaderFTDI 2232HL
User IOs 827 + 741 + 24 + 218
Pmod Connectors 1 (dev board)3001
User Buttons 01 tactile + 3 tactile on breakoff Pmod1 reset2 capacitive0
User LED 1 tricolor, high-power IR LED2 on-board4 on dev board2 + 5 on breakoff Pmod5
Onboard Clock 12 MHz, shared with FTDI12 MHz, shared with FTDI16 MHz12 MHz12 MHz, shared with FTDI
Flash 8 Mb QSPI128 Mbit QSPI DDR8 Mbit SPI16 Mbit SPI32 Mbit SPI
IMU InvenSense 6 DOF (accellerometer + gyroscope)NoNoNoNo
Mic MEMS I²S microphoneNoNoNoNo
Power Measurement On dev kitNoNoNoNo
Open Source Toolchain YesYesYesYesYes
APIO YesYesYesYesYes
Icestudio YesYesYesNot yetYes
Migen NoYesYesNoYes

Support & Documentation

All data about the SoM is captured in the Vision FPGA SoM GitHub repo and will be updated over time with sample code, Colab notebooks, etc.

Credits works closely with clients to incorporate low power CV into their devices. We are enabling CV with hardware in the form of tightly integrated CV modules with a clean API.

Venkat Rangan

Emcraft Systems


Pixart Imaging



Component provider

See Also

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