throwaway_posit

4 ms·

1. 1.▲
   by throwaway_posit 5y ago

   If you're familiar with how the hidden bit works for IEEE floating point, use a hidden '10' in front of the fraction for negative numbers for posits and suddenly a whole bunch of math falls out. This is equivalent to having
2. 2.▲
   by throwaway_posit 5y ago

   Oh I actually only think it's useful for machine learning. I have some unpublished, crudely done research showing that the extended accumulator is only necessary for the Kronecker delta stage of the back propagation (posits trivially
3. 3.▲
   by throwaway_posit 5y ago

   Sorry, should have specified: I'm the original implementor. I'm on the paper with John Gustafson, and presenter/live-demoer of the second half the Stanford video. There is a paper coming out with details on the yonemoto -2
4. 4.▲
   by throwaway_posit 5y ago

   I will say one thing: I pity the person (grad student?) that has to do error propagation analysis on a research project using posits (I'm the original implementor)
5. 5.▲
   by throwaway_posit 5y ago

   Yep. This works out to be on the order of 60x6x2 adders which is honestly not that much.
6. 6.▲
   by throwaway_posit 5y ago

   In your RTL synthesis did you use "hidden -2 bit" for negative posits? Assuming you are "cheating" IEEE by not implementing subnormals or NaN... This is one key insight that makes posit sizes much smaller, but the algeb
7. 7.▲
   by throwaway_posit 5y ago

   I'm the implementor for posits (deactivated my primary hn account) -- I built circuit diagrams for computation with posits. So, we have them -- and they are smaller. There is also a key insight into the representation of posits (nega