Deep learning on the Raspberry Pi!

raspberrypi

Photo by Clive Darra

I’m very pleased to announce that I’ve managed to port the Deep Belief image recognition SDK to the Raspberry Pi! I’m excited about this because it shows that even tiny, cheap devices are capable of performing sophisticated computer vision tasks. I’ve talked a lot about how object detection is going to be commoditized and ubiquitous, but this is a tangible example of what I mean, and I’ve already had folks digging into some interesting applications; detecting endangered wildlife, traffic analysis, satellites, even intelligent toys.

I can process a frame in around three seconds, largely thanks to heavy use of the embedded GPU for heavy lifting on the math side. I had to spend quite a lot of time writing custom assembler programs for the Pi’s 12 parallel ‘QPU’ processors, but I’m grateful I could get access at that low a level. Broadcom only released the technical specs for their graphics chip in the last few months, and it’s taken a community effort to turn that into a usable set of examples and compilers. I ended up heavily patching one of the available assemblers to support more instructions, and created a set of helper macros for programming the DMA controller, so I’ve released those all as open source. I wish more manufacturers would follow Broadcom’s lead and give us access to their GPUs at the assembler level, there’s a lot of power in those chips but it’s so hard to tune algorithms to make use of them without being able to see how they work.

Download the library, give it a try, and let me know about projects you use it on. I’m looking forward to hearing about what you come up with!

14 responses

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