2 ms·
I agree with you that Posits only really make sense for <=32 bits. That said, I disagree that the optimal distribution of numbers is logarithmic. Especially for
by adgjlsfhk1 4y ago
I agree with you that Posits only really make sense for <=32 bits. That said, I disagree that the optimal distribution of numbers is logarithmic. Especially for 16 bit or smaller computations, if you are dealing with big or small numbers, you need to renormalize to roughly 1 anyway to prevent overflow and underflow. As such, it makes a lot more sense to have extra accuracy near 1.
- adrian_b 4y agoI agree with you that for small precision logarithmic distribution may not be optimal and that is why posits may be a better choice for such applications. Logarithmic distribution is optimal for complex physics models where there are relationships between many different kinds of physical quantities, so it is not possible or convenient to choose a scale factor that would make all quantities have values close to 1. In such applications many numbers are very large or very small and any other distribution except logarithmic leads to increased errors in the results. Such models also typically need a 64-bit precision. Moreover, having a much greater exponent range than provided by the 32-bit floating-point format, in order to avoid overflows and underflows, is frequently even more important for them than having a greater precision. If for an application the range of the 32-bit floating-point numbers is good enough, then there are indeed chances that posits might be better for it.