Deep Neural Network on Embedded?

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I recently implement couple of algorithms for Deep Learning, which provides fast inference on an embedded devices.

One of them is called ‘LCNN’, you can find the original paper here : [1611.06473] LCNN: Lookup-based Convolutional Neural Network

And I implemented it with tensorflow,

https://github.com/ildoonet/tf-lcnn

This codes compress alexnet which takes roughly 150ms or more on a single core cpu,

to a sparse convolutional layered network which takes 10~50ms on the same environment.

image
image858×300 14.7 KB
https://github.com/ildoonet/tf-lcnn/raw/master/images/timeline_alexnet.png

So.. based on these new technologies, i am looking for an idea to try.

Like openpose on a robot : Human Pose Estimation Deep Learning Model (OpenPose) ROS Package - Projects - Open Robotics Discourse

Any Thoughts?

2 Likes
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@ildoonet, that’s pretty cool. What embedded platform are you working on? The results are pretty interesting. Well done.

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It’s for snapdragon cpus like smartphones.

Also I’m working on arm cpus.

Also, this codes is not optimized for specific cpu architecture, It can be improved further.

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That’s really cool!

Do you have trained neural nets to go along with it?

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I have alexnet which 20 times faster than the original one. I will add more models. Do you have any particular model you want?