To get the deep-learning algorithms compressed enough to fit on the RPi 3 using just a few bits, Ofer and his team employed a technique known as sparsification, a technique that shave's off unneeded redundancies. Doing so allowed them to devise an image detection system that could process 20-times faster on limited hardware without losing any accuracy. Still, the team hasn't yet figured out a way to take ultra-sophisticated AI or a deep-neural network and compress it enough to fit on limited, low-powered hardware. Regardless, this is an unprecedented first step in doing so, and we can certainly expect advancements that will get us there sometime in not too distant future.