[scarmig]

Well, with all 16 logic gates available, they can express all Boolean circuits (you could get that even with NAND or NOR gates, of course, if you are working with arbitrary as opposed to fixed connectivity). And so you could have a 32 bit output vector which could be taken as a float (and you could create any circuit that computes any bitwise representation of a real).

As for efficiency, it would depend on the problem. If you're trying to learn XOR, a differentiable logic gate network can learn it with a single unit with 16 parameters (actually, 4, but the implementation here uses 16). If you're trying to learn a linear regression, a dumb MLP would very likely be more efficient.