The i.MX6 SoloX processor is fairly unique in the i.MX6 family primarily because it co-hosts a single Cortex A9 along with a Cortex M4. The heterogeneous architecture proves very useful for hard real-time processing occurring on the M4 while concurrently running a Linux stack running on the A9 (the heterogeneous architecture is implemented on the i.MX8 line of processors). In previous posts using the UDOO Neo I have covered how these features can be exploited when interfacing different peripheral devices. The processor architecture lends itself nicely to IOT (Internet of Things) Edge devices where sensor capture and data preprocessing/conversion can occur on the device before being forwarded to the cloud where a richer set of analytic processing can be performed. If we could perform some (or all) of the analytic processing on the edge device then we might dramatically reduce the amount of device data traffic sent to the cloud. Alternatively the edge device could make decisions for itself and not completely rely on the cloud, furthermore it opens ups the possibility of the edge device partially functioning when the network isn't available. This concept is know as Edge Analytics.
A single Cortex A9 practically isn't up to the job of performing intensive analytical processing especially if we would like to implement a machine learning algorithm. In terms of machine learning techniques Neural Networks are one branch that has gained considerable popularity in the last few years primarily because it offers new avenues for the types of analytical processing that can be done ie image recognition or text processing. The Movidius Neural Compute Stick (NCS) is an intriguing concept as it opens up the possibility of deploying deep neural networks on embedded devices. In the video we demonstrate feeding a number of images (loaded from png files) to a caffe GoogLeNet model, for each inference it displays the top matching label and probability score. As a performance enhancement we utilise the PXP engine to perform hardware image resizing and BGRA conversion before feeding a 224 x 224 image to the model for classification. The resized image is also rendered to the screen (using the 2D acceleration). To gain an acceptable level of performance the application was developed in C/C++.
So, the first challenge was to see if we could get NCS running with the UDOO Neo (the i.MX6SX board). My starting point was referring to the Movidius article of deploying on the Raspberry PI. As mentioned in the article its important to highlight that training, conversion or profiling of the Neural Network can't be done on the embedded device ie "Full SDK mode". This implies that the Neural Network needed to be trained and converted using a standard PC or cloud environment. Deployment to an embedded device is restricted to "API only mode". This first step turned out to be a challenge mainly because my starting pointing was Ubuntu 14.04 and not 16. It took a few days to get the correct packages compiled and installed before caffe would compile without errors. The Neural Compute Application Zoo provides a number of sample applications, you can use hello_ncs_cpp or hello_ncs_py to verify the OS can communicate with NCS. The other gotach is that the NCS is power hungry and requires a powered usb hub especially if you have other usb peripherals attached. On the NEO the NCS can be plugged directly into the USB type A socket if you don't have a need for additional peripherals.
The second step was to see if we could deploy a Neural Network graph on the NCS and perform simple inferences. Most of the sample applications in the 'Zoo' are Python based with some having further dependency on OpenCV. Unfortunately running OpenCV and Python on Neo would introduce too much of a bottleneck with regards to performance (or in fact most low power ARM embedded devices). The 2 reasons for this are the single A9 core and the fact that the X11 interface doesn't support hardware accelerated graphics. With ARM processors there is trade off between power and performance and for 'always on' IOT devices this does become a major deciding factor. Fortunately caffe provides a C++ interface although there's little documentation available about the API interface. Within 'Zoo' there is multistick_cpp C++ application which demonstrates communicating with multiple NCS devices.
1. Load png, resize and convert : approximately 800 milliseconds
2. NCS inference : approximately 130 milliseconds
We can't do much about Step 2 without re-tuning (a redesign) the Neural Network or by reducing the image size (possibly leading to less accuracy). Step 1 is slow because the file images are roughly around 800x800 pixels and software resizing to 224 x 224 is painfully slow. Fortunately we can address the resizing and conversion time in Step 1, the i.MX6SX contains an image processing unit known as PXP (Pixel Pipeline) which can rescale and perform colour space conversions on graphic buffers. I re-factored the code in step 1 as below:
1. Use libpng to read the png file
2. Resize and color space the image using PXP
3. 2D blit re-sized image to screen
With the above changes sample timings dramtically improved for step 1 (as show in the video):
1. Load png, resize and convert : approximately 233 milliseconds
2. NCS inference : approximately 112 milliseconds
Hopefully this article provides useful introduction to deploying the NCS with i.MX6 or i.MX7 line of processors. Going forward I would like to get a camera working with Neo and see what FPS rate we can achieve. The other interesting avenue is deploying SSD MobileNet and using the PXP overlay feature to render matches.
I liked to thank motiveorder.com for sponsoring the hardware and development time for this article.