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Movidius X Carrier Board for Raspberry Pi Compute Module

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Using the Raspberry Pi as a host for the Intel NCS2 (Myriad X) is becoming an increasingly popular for running neural inference on the 'edge'.

However the data path when using an NCS2 is inefficient because the camera data has to flow through the Pi first to reach the NCS2. This results in a ~5x reduction in performance of what the NCS2 (the Myriad X) is capable of.

So what we're making is carrier board for the Raspberry Pi 3B+ Compute module, which exposes dual-camera connections directly to the Myriad X. Then the Myriad X connects to the Raspberry Pi, largely over the same manner it does when in the NCS2 (so much code can be reused).

This allows a couple things:
1. The video data path now skips the Pi, eliminating that CPU use (which is a LOT).
2. The hardware stereo-image depth capability of the Myriad X can now be used.
3. An estimated ~5x improvement on MobileNet-SSD object detection, as a result of the Raspberry Pi CPU no longer limiting the X.

So the effort that prompted us making the Raspberry Pi ("AiPi") solution is actually Commute Guardian (check it out at  So we have a lot of that effort (prototyping, results, etc.) mixed in here.

We have to do all (and more) of the work for the AiPi for that end-goal (which is itself to save bikers' lives).  And so we figured, why not share the general underpinnings to Commute Guardian, here, with Hackaday - so others can benefit from the core work we're doing on Commute Guardian.

For more information on what we're thinking on the AiPi, stay tuned here, and/or check out to give feedback/feature-requests/etc. as we progress along making the device.

  • 1 × Intel Myriad X Vision/AI Processor
  • 1 × CM3B+ Raspberry Pi Compute Module 3B+
  • 2 × Camera Connectors TBD, comments welcome!

  • First Boards Ordered! Modular Design in Progress

    Brandon04/29/2019 at 18:14 3 comments

    Hey everyone!

    So we did it.  We ORDERED our first boards.  Man, low-volume pricing on tight-tolerance boards is so painful.

    Fortunately we think our kids are crafty, so they won't need a college fund, right?

    We're actually going with a modular approach from what we learned from this, so that we can make various hardware incarnations, which will all leverage the same Myriad X module.

    That way we don't have to give up our kids' college funds every time we decide to change features/form-factor/etc.

    So that's what we're currently working on.  The current for the module are 2 x MIPI 4-lane which are usable as 4 x MIPI 2-lane and of course USB and all that jazz.

    The idea is that this allows up to 4x 2-lane cameras, 1x 4-lane camera and 2x 2-lane cameras, or 2x 4-lane cameras.

    Which we think should cover any/all permutations of the boards that this module will go on.  If there's interest, we'll sell the module alone as well.


    And as a bonus, here's one of the camera modules, which is a greyscale stereo pair:

  • Hardware Depth and Video Tracking Working

    Brandon03/18/2019 at 16:19 0 comments

    Hey guys,

    So we got hardware depth and video tracking working.  It's not calibrated depth yet (so that's why it doesn't look so great - it's using a unity 3x3 homography matrix).  But it's working!  (Caveat on that, it's still buggy and crashes on startup 9/10 times, but the 1/10 is so satisfying!)

    But to re-iterate, all the calculation shown in the video is being done on the Myriad X (depth calculation and feature tracking).  The host is doing nothing (other than just displaying the data that the Myriad X is streaming, which is optional).  

    The nice part is the Myriad X doesn't even get warm doing this.  And that's with zero heatsink.  Just the chip exposed to ambient air.

    And for more info as to our end goals, check out: - a Raspberry Pi depth vision + AI carrier board, which is itself a product we thought would be useful to the world, and is an internal stepping stone to: - the AI bike light to save lives

  • We Have a Logo!

    Brandon03/05/2019 at 05:30 0 comments

    So we've been chasing down parts for our first board run (of 10 prototype/dev.) units, and in the meantime we now have a logo, thanks for our fancy graphic designer:

  • First Board Design | Component Placement Done

    Brandon02/19/2019 at 03:53 3 comments

    Hey Embedded AI Enthusiasts,

    We're excited to share that we just finished component placement and initial routing of our first version of the board.  This one is for initial development, debugging, etc. - and actually doesn't even have a Raspberry Pi slot yet.  It'll primarily be programmed by JTAG and prodded and debugged.

    Anyways, here's a 3D view of it:

    It is, however, the same size as a Raspberry Pi 3.  For the later versions, we'll remove a TON of extra stuff that's on this one - so there'll be more room for the Raspberry Pi CM3B+ module.



    AiPi Team!

  • Bicycle Safety Survey

    Tegwyn☠Twmffat02/18/2019 at 10:00 1 comment

    To help take this product further, please could people fill in the short survey - should take no longer than 60 seconds:

  • Ordered Our First Myriad X Parts

    Brandon02/16/2019 at 02:01 0 comments

    Hey Machine Learners,

    Exciting news!  We ordered our first set of Myriad X parts today, for our initial round of internal development/verification boards.

    Only 10 units so far, as their just for internal use.

    And if all goes well, we’ll be able to order the boards for manufacture/population in a couple weeks.



  • Clearer View of What AiPi Sees

    Brandon02/13/2019 at 15:58 0 comments

    This demo is with an Intel RealSense D435 + Raspberry Pi 3B + NCS1.

    It's doing MobileNet-SSD Object Detection and depth-data projection to give XYZ position of every pixel.  And we're printing the XYZ of the middle-pixel of bounding boxes in the bounding box label (hence with the chair, it changes when I walk behind it, because the center-pixel is actually the wall behind the chair in its initial orientation).  All other pixels' XYZ are available per frame, so you can use the ones most pertinent, average over an area, etc.  And in the case of the Commute Guardian, the XYZ location of the edge of the vehicle is used for impact prediction.

    We're working to make a board which leverages the Myriad X to do the depth calculation (and de-warp/etc.) directly while also doing the neural network side (the object detection).  This should take the whole system from ~3FPS to ~30FPS, while reducing cost.

    And if you want to give input on what the design should be, or other designs you'd want instead (or generally just to find out more options for embedded machine learning), head over to:

    And if you want to know the background/why of us making this stuff, the end goal is to save bike commuters' lives:

    We're simply releasing our work, before the final bike product is out because we realized that the board itself (particularly with the Raspberry Pi as the brain) would be super useful for a bunch of engineers across a variety of project types.

  • AiPi Forum Up!

    Brandon02/11/2019 at 20:33 0 comments

    Hey guys,

    So in our efforts we found it actually pretty hard to know all the options out there for neural processors/etc.  So we figured having a forum to discuss these would be great.

    And even greater, a place to drop your needs/etc. which aren't met by current solutions (which is super common as embedded machine learning is just really starting).

    So without further ado, here is our forum for that:

    And I figured I'd start everyone off with the summary of parts of interest we've found so far, and links to some other useful resources which cover even more (WAY too many) parts.



  • Commute Guardian Forum Up!

    Brandon02/11/2019 at 00:16 0 comments

    Hey gals and guys!

    So the Commute Guardian forum is now up!  Come join the conversation WRT saving bikers' lives!:



  • CommuteGuardian UI/UX and Rider Early-Warning

    Brandon02/10/2019 at 23:08 0 comments

    Hi everyone,

    Quick background: AiPi is us sharing a useful product we're developing on our path to make the product.  Here's some background on that product:

    Wanted to share the idea of how the rider is warned, before the horn goes off. The background here is the horn should never have to go off. Only the MOST distracted drivers won’t notice the ultra-bright strobes, which activate well before the horn will activate.

    However, the horn WILL go off should the driver not respond to the strobes. It will activate early-enough such that the driver still has enough time/distance to respond and not hit you.

    To warn the rider, there are two separate systems. The first one is an optional user interface via smartphone, which we’ll discuss first as it paints the picture a little easier:

    So this gives you the states. In normal operation, it is recording and gives you map operation.

    When there’s a warning state, the ultra-bright strobes come on, and there’s an overlay to make you aware of the elevated danger.

    An example of this is a vehicle on your trajectory at a high rate of speed, which is still far away.

    And when that vehicle is closer, the strobes didn’t deter them from an impact trajectory, the horn will sound, and you’ll be visually warned.

    So the -second- system of warning the rider doesn’t rely on this optional (although cool) app.

    It’s simply an audible alert that the biker will hear (but the car likely won’t) that will sound in the WARNING state, to alert the biker of the danger, and hopefully bring them into a state of being able to avoid the DANGER state (moving over, changing course, etc.)

    Thoughts? [Feel free to add comments!]



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psykhon wrote 03/07/2019 at 12:19 point

Hi Brandon, awesome project! 

How hard was to get te myriad x chips? Can you share some info on how do you do it? price?

  Are you sure? yes | no

Tegwyn☠Twmffat wrote 02/01/2019 at 14:53 point

I checked out that link above - I wonder how their larger kit compares to the Jetson TX2 in terms of performance?

I realise performance is not everything and the Intel model zoo is pretty useful. The Nvidia software seems to be a bit behind in that they only have blvc_Googlenet as 'out of the box' solution for detection.

What do you think your price point will be for a single myriad X carrier board, I'm presuming about $100 ?

  Are you sure? yes | no

Brandon wrote 02/01/2019 at 15:02 point

Great question!  So we've actually done a decent amount of stuff on the Tx2 as well.  The Myriad X, in terms of straight neural inference performance (e.g. object detection, semantic segmentation, etc.) is about the same as the Tx2.  The Myriad X neural engine is 1 TOPS, and the Tx2 peaks in ideal conditions at 2 TOPS, but from below, it seems like in most conditions, it's effectively 1 TOPS:

But!  If your application is depth vision + neural inference, the Myriad X is equivalent to about 2 or 3 Jetson Tx2, mainly because of the 16 SHAVE cores in the Myriad X, which together can do 6 cameras in 3 pairs of depth streams. 

The neural inference part of the Myriad X is only 1 TOPS of the total 4 TOPS the device an do.  The remaining tops are for image processing functions like depth vision.

So this board won't really even tax the Myriad X, as there will just be one depth stream.  That said, we can use the extra Myriad X 'head room' to run fancier/more-processing-intensive depth calculation on these just 2 cameras - to produce a better set of depth information.

  Are you sure? yes | no

Tegwyn☠Twmffat wrote 01/31/2019 at 22:58 point

Hello Brandon! Does the Myriad X chip get put on the carrier board or does it stay in the USB stick?

If it goes on the board, how many of them?

  Are you sure? yes | no

Brandon wrote 02/01/2019 at 12:37 point

The Myriad X would be directly on the carrier board.  We could make versions with multiple Myriad X, for sure.  Is that of interest?  

These guys did that for their PCIE version:

I have 2 of those on order, by the way.  They're useful as well, for sure - just a different application, and not applicable for the Pi community (which is what this board should serve).

  Are you sure? yes | no

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