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I see this uses a VisionBonnet board based on a Movidius chip... I wonder if such an accelerator is really needed. Could someone elaborate on the resources need
by captainmuon 9y ago
I see this uses a VisionBonnet board based on a Movidius chip... I wonder if such an accelerator is really needed. Could someone elaborate on the resources needed for applying (as opposed to training) a typical deep neural network? I am only familiar with "old school" neural nets, which are relatively cheap to evaluate. What is the bottleneck, memory, or the parallel computation? I would think that you could use GPU shaders to some extent, but the mixing after each layer and the number of weights you have to save would make it hard on a RPi, right?
- Eridrus 9y agoNeural Net accuracy scales with the amount of compute you have available at inference time, and for vision models it is largely the amount of compute you need (NLP models often require a lot of memory too), so while you could probably squeeze something onto a raspberry pi, you would probably have to sacrifice frames per second and overall performance. I think for a hobby project bundling an accelerator is the right choice so that hobbyists don't have to worry so much about performance. The same way that a raspberry pi really is overkill for almost everything people do with them since you could do the same with a microcontroller with no OS, someone could probably squeeze something onto a raspberry pi without the accelerator, but that's going to be far harder than just getting started with the high level APIs.