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Daniel Situnayake: AI on the Edge
The Gradient: Perspectives on AI
How to Quickly Deploy Machine Learning Into Your Embedded Engineering Workflow
How do you take something that you've trained and get it onto the device you're targeting in a straightforward way when there are literally thousands of potential devices out there? A big part of this problem is building a pipeline that can take a model and then spit out C++, which is kind of the lingua franca for these devices. And so what we've probably spent the most time on overall is getting that process right. The nice thing is you don't have to be the fastest. You don't need to whittle out every last tiny little bit of performance. But what you do have to do is be portable and convenient and allow people to come in like often you're sort of
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