I'm going to raise again a very general question relating to Eigen/Eigen3 matrix library support for different matrix support "fields/representations" for operations.
I've analyzed a bit the Eigen matrix template library, and so far, I've only seen suffort for floating point real numbers arithmetic (that is IEEE754 single precision 32 bits and double precision 64 bits floating point numbers).
I would like to raise a question concerning fixed-size precision real numbers arithmetic support for Eigen/Eigen3:
- is there any support for fixed precision vectorization in Eigen/Eigen3 ?
- if not so, what would be necessary to implement such a suport ?
- can standard decomposition routines and matrix operations be immediately implemented using fixed size precision ? If so, how ?
- if not so, what are the pre-requisites for such a support (concepts, operators overloads, "real" functions required to be implemented, etc...) in order to implement such operations/decompositions without impairing Eigen's core ?
- are there any plans to implement such functionnalities into the core of Eigen/Eigen3 ?
If such kind of things aren't foreseen in the near future,
- does there exist already any kind of such functionnality that you are aware of and that would be compatible with Eigen/Eigen3 in order to fully implement vectorization/optimizations ?
- if not so, which approach would you recomment if s.o. was interested in implementing it ?
I would like to know the feasibility to implement a few matrix computations onto a 16- or 32-bits micro-controller. I'm not aware of any such kind of things that are disclosed under GPL licencing scheme, and would be geatly interested if such thing would be usable. If not, I would like to assess workload necessary to implement it.
Thanking anyone in advance for help.