This is the second part of the series of articles about Computer Vision for mobile and embedded devices. Last time I discussed ways to optimize Image Preprocessing even before you try to get ML model inference directly on the device.
Inbounds of this article I am going to talk about the most crucial step —
on-device ML model execution
What is the right ML tool for mobile?And this is a really good question! The answer to that could be found by going through the next items :
Let’s take a look closer to the two most popular mobile platforms.
Apple gave iOS developers a brilliant gift — CoreMl. But it is not the only solution for this mobile platform. So what options do we have, let’s check it one by one:
Pure Apple solution is** **CoreMl
Tensorflow Lite associated in our mind with Google (Android) technologies but it could be a solution for iOS platform.
In terms of usage and performance, Caffe2 is similar to TF Lite but much more flexible in the converting process. Using ONNX as the middle format you can easily bring your server-side model to iOS platform.
As I mentioned above — efficient on-device ML inference is quite a hardware-specific task and it makes a lot of troubles for Android devices. Nowadays there are more than 16 000 Google Play Certificated devices (device models), overall more than 24 000! Each model can have its hardware as well as software specifications. So the answer to the question of “the right tool” selection can be application-specific.Let’s take a look at our options and a short description for them.
Tensorflow Lite is the most promoted by Google Android ML tool and there is a set of reasons for that.
Qualcomm Neural Processing SDK for AI is a brilliant example of excellent developers support by the hardware vendor. Qualcomm provides us with a set of efficient tools to establish the whole pipeline of ML processing on the device. I am not talking only about their fast-performed ML libs but also about tools for Digital Signal Processing, Video Stream processing, compilation, etc.
HUAWEI HiAI is one more example of hardware-specific solutions. The good thing about this product that it has Android Studio plugin which makes a lot of work for you but bad things — that this plugin has plenty of bugs. Anyway, you have an opportunity to convert your ML model and generate Java code to use it using the UI tool.
Performance: It works with a high level of performance through Metal shaders, directly on mobile GPU (or specially dedicated for ML operations hardware for latest models of iPhone).
ML operations (layers) capability: Almost all modern server-side ML frameworks have prepared scripts for converting to CoreMl format. Even if the converter does not support necessary layers you can write these operations by yourself using Metal shaders. But be careful with that! From the **second generation of CoreML **tool, it is better to use only layers “from the box” at least for the latest iPhones (iPhone XS, XS Max, and XR). In that case, all the operations will be executed on special hardware, which will lead to fast performance and less power consuming. Custom Metal-shaders operations will bring your ML model **back to GPU **and you will not get advantages of CoreML 2 and above.
Hardware specifications: As soon as all iOS phones have almost the same hardware vendor it is a simple topic. We have around 10 specifications which support the same kind of technologies.
platform by HiSilicon (part of Huawei). A surprising fact for me that each version of HiAI library is designed only for one Kirin model — so if you add HiAI 2.0 to your app it will work only with Kirin 980 and for an older model you should use the older version of HiAI. I guess it makes troubles on the way to use it in production.
MACE by XiaoMi is a good try to create unified ML solution for ARM-Based devices.
Caffe2 is also can be an option for Android devices. Its CPU runtime performs well — in many cases better than other frameworks CPU runtimes. And as we remember it is optimized for ARM-Based devices so it should work for almost all Android phones.
From all the above, you can see that the term “The right mobile ML tool” really depends on the needs of your app. It is a good idea to investigate the potential market, find out mobile hardware and software dominance before you start your mobile ML project.In the next article, I am going to talk about the potential outcome of ML models in **Computer Vision **and ways to efficiently process it. **Output post-processing **can be a tricky thing in terms of application performance.
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