There is an algorithm in SoftPosit to convert various precision posits to various precision floats, and one for floats to posits. It's clearly NOT a silly question, because I've been informed that Oxford professor Milan Kloewer, who uses 16-bit posits to do weather and climate modeling, found a way to do conversion much faster, at least for the precisions he uses. Like, 20 times faster. Look on Github for his library… his method is open-source. I can see how converting 16-bit posits to 32-bit floats could be super-fast because you know the 32-bit floats are a superset of the 16-bit posit vocabulary, and you won't experience overflow or underflow or subnormals. Going in the other direction is trickier but I think he figured out a way to do that quickly as well. Encoding the regime value k into a run of 0 or 1 bits just involves some shifting by k and integer ops. In general you have to round, though, which will add a clock cycle or two. Since SoftPosit is general for posit precisions up to 32 and float precisions up to 64, it first decodes the posit or float into the sign, the integer exponent, and the significand, and then re-encodes those into the other format with tests for exception conditions. Obviously, that's going to be a lot slower than it needs to be when converting 16-bit posits to 32-bit floats. Another reason this is not a silly question is that posits may offer immediate bandwidth relief even before we have modern CPUs with full custom VLSI support for posits. If you can build float-posit convertors between 32-bit posits and 64-bit floats, or 16-bit posits and 32-bit floats, then posits can serve as a compression method for floats stored in memory. This has already been considered, for example, by the Square Kilometer Array project.