[btashton]

I thought for this the standard practice is fixed-point. Requires more planning and mental gymnastics but usually much faster and gives you full control of the precision.

Maybe this has changed from my DSP days.

[krapht]

I am confused. If you used to work in DSP then you know the standard practice involves MATLAB, where the default is double precision. This is also the default datatype in NumPy. Most engineers don't like working in fixed-point unless they have to for other reasons, like moving it onto an FPGA or something.

[mng2]

The person you're responding to probably worked with DSP chips, which are generally not floating-point. e.g. Motorola 56000, TigerSHARC, Blackfin.

[Keyframe]

Why not? Because of interop with other libs/tools? It's not _that_ hard, but I can see the problem if whole workflow isn't like that.

[l33tman]

You know a cool thing you can do to help fixed point interop between operations in a complex system where you absolutely don't want to accidentally overflow anywhere? You can tack on some bits to the number to control an overall scale of the fixed point number. Let's call it an exponent ;)

[Keyframe]

Agreed. Let's standardize it :))

[btashton]

most of it was FPGA work but in some cases DSP processors. Even when you have floating point support it is much slower than if you use fixed point. As for MATLAB, modeled plenty of filters in it for fixed-point math.