
Hi Tomii  as you point out, the signbit in the IEEE 754 is just 0 = positive, 1 = negative; that is, the combination of the sign bit and the mantissa is a signmagnitude vale as opposed to being a two's complement value. One problem with thsi is that you can have both +0 and 0 values. Have you any idea why they chose to do things this way?
Re: Signed magnitude
tomii
1/7/2014 4:33:16 PM
@Max:
Sorry for typos  answering from cell phone, as I'm travelling...
Yes, I actually do know why. Pos & neg zero are important for completeness. This allows a divide by zero to give a correct answer  that is, pos or neg infinity
Re: Signed magnitude
betajet
1/7/2014 4:47:25 PM
I believe the main reason for S/M representation is that it simplifies multiplication and division. 2's complement multiplication is a pain and requires more logic (IIRC) and division is hard enough without dealing with signs. With S/M, you just do unsigned multiplication and division and then XOR the sign bits.
2's complement arithmetic is good if you're always adding signed numbers, but if your logic needs to both add and subtract, then you've got to complement one of the operands anyway. I believe floatingpoint hardware does S/M add/sub using one's complement arithmetic  I remember studying the IBM 360/91 paper that shows this in detail. The fun part of one's complement arithmetic is the endaround carry. To do this fast, you need carry lookahead endaround carry logic, which turns out to be highly regular and beautiful.
Regarding +0 and 0: I should think the floatingpoint compare instructions take care of this. If you try comparing floatingpoint numbers using fixedpoint compare instructions you deserve what you get :)
@betajet: If you try comparing floatingpoint numbers using fixedpoint compare instructions you deserve what you get :)
LOL
Re: Signed magnitude
tomii
1/8/2014 8:37:33 AM
@Betajet:
I believe the main reason for S/M representation is that it simplifies multiplication and division. 2's complement multiplication is a pain and requires more logic (IIRC) and division is hard enough without dealing with signs. With S/M, you just do unsigned multiplication and division and then XOR the sign bits.
Shhh! Spoilers!
754 is a really powerful standard, and it includes lots of really clever stuff to squeeze the most out of the format, but the result is that the floatingpoint library C/C++ code required to perform the math operations is pretty large.
One alternative, as you say, is to "roll your own". I've often toyed with the idea of creating my own 16bit (Half Precision) library (providing reasonable dynamic range with limited precision, which would be applicable to certain applications) with strippeddown functionality so as to reduce the memory footprint ... but there never seems to be enough time ... I wonder if any of the other readers have done this?
Re: 754 is great, but...
anon5532556
1/8/2014 3:22:44 PM
Speaking of home built libs, I made some rough 16 bit floating point stuff a long time ago during the dinosaur micros. Also a couple of crude 12 and 8 bit versions. Don't laugh, it was sometimes kinda useful for working with curves like audio gains etc. And less bits made certain lookup tables possible, giving mathless math and fast calcs to a slow bit banger if you had a bodacious eprom etc.
Nice write up btw :)
PS: Anybody else use the AMD 9511 or 9512?
Re: 754 is great, but...
TanjB
1/11/2014 2:39:00 AM
Those were AMD bitslice micros? I used the Intel 300x and the AMD 291x, which were 2 and 4 bit slices.
16 bit FP is making a comeback. You can find it supported in some current GPUs. I believe it is used mostly to represent high dynamic range graphical data but there are probably other uses.
Of course, 8 bit FP was actually hugely important. The Alaw and mulaw codecs used by all phone networks in the ISDN days, still used in some landlines and voice exchanges, were essentially FP with a sign, 3 bit exponent, and 4 bit fraction (with implied leftmost 1, just like IEEE formats).
Re: 754 is great, but...
anon5532556
1/13/2014 3:11:34 PM
Not bit slice. T did use the AMD 2901 and 29116 for a while though.
The 9511 was a 32 bit floating point chip with 8 bit bus. Easy to hook to a Z80 etc. It directly handled a bunch of curve type functions and the like. Mostly used it for sin/cos/tan things doing earth curvature work. The 9512 was much simpler but wider inside. Both ran hot, and cost a lot.
Oddly enough it looks like MicroMega currently sells an FPU for microcontroller projects. That has to be going away though, as the ARM 32F4 part I'm using today does floating point so fast I regularly use it in interrupt routines.
I think the thing that confuses a lot of beginners with regard to floating point is why we bother to do it in the first place.
Let's start with the fact that whatever format we decide to use to represent numbers inside a computer, they all end up being stored as a sequence of 1s and 0s.
The thing is that, given a field of a fixed size, what sort of numbers can you store in it? Let's take the arduino, because that's what I'm playing with at the moment. Consider a variable of type "long"  this consumes 32 bits (4 bytes) and can be used to store integers in the range 2,147,483,648 to 2,147,483,647. These are big numbers and they are stored with absolute precision (so long as we only want integers), but what if we want to represent some value outside this range?
This is where floatingpoint comes in. A floating point value in an Arduino also consumes 32 buts (4 bytes), but using the format you discuss (sign, mantissa, exponent), it can be used to represent values as large as 3.4028235E+38 and as small as 3.4028235E+38. This gives a humongous dynamic range, but at the loss of precision (these values have only 67 decimal digits of precision).
Re: When you have limited bits to play with...
betajet
1/7/2014 4:57:00 PM
As Tom points out, floatingpoint is similar to scientific notation. Indeed, one of its main uses historically has been for scientific number crunching. In science, you're always working with numbers you have measured with limited accuracy, so there's limited value in performing calculations more precisely than the figures going into them. What's the point of calculating the resistance of a resistor network to 20 digits of precision when they're at best 1% resistors?
Decimal floating point...
cpetras
1/7/2014 5:04:49 PM
The IEEE 754 standard (2008) has introduced decimal floating point. http://en.wikipedia.org/wiki/IEEE_7542008 http://en.wikipedia.org/wiki/Decimal_floating_point htttp://www.cl.cam.ac.uk/~jrh13/papers/decimal.pdf Which is really great if you need to do anything financial.
@cpetras: The IEEE 754 standard (2008) has introduced decimal floating point.
Didn't it actually introduce multiradix floatingpoint, of which decimal is one incarnation, or was decimal singled out?
Re: Decimal floating point...
TanjB
1/8/2014 11:51:31 AM
Yep. And in practice decimal FP is not ideal for financial calculations.
FP calculations (in any radix) are common in engineering, science, and anything approximate. Even in finance they are perfectly fine to use in situations like estimating future or present value, or allocating budgets.
When it comes to accounting for the cents, however, fixed point is more likely what you want. Most of those operations are multiplies, adds and subtracts, which are exact in fixed point, with the occasional fraction like taxes which have rounding rules built in.
And as for the need for precision in a world where resistors might be accurate only to a percent, it is amazing how easy it is to get yourself into trouble with the math once you start doing simulations and (much, much trickier) optimizations. Simple components like transformers are nearly singularities. Numerical optimization packages are black arts mostly because of the clever tweaks needed to efficiently detect and work around problems with the limited (!) precision of 64 bit doubles.
@TanjB: ...as for the need for precision in a world where resistors might be accurate only to a percent, it is amazing how easy it is to get yourself into trouble with the math once you start doing simulations and...
VERY good point!!!
Re: Decimal floating point...
betajet
1/8/2014 2:07:22 PM
If your calculations are becoming unstable even with double precision, it's time to step back and do a proper numerical analysis of your problem. Here's what too many people forget: floatingpoint numbers are not real numbers, so the normal laws of real numbers  like associativity of addition  do not apply. When you add a tiny floatingpoint number X to a big floatingpoint number Y, all the bits of X fall into the bit bucket and you end up with Y, not X+Y. Sometimes you need to use algebraic tricks to rewrite your formulas into expressions that are stable for your problem and hope the compiler doesn't "optimize" them.
I've read that John von Neumann greatly disliked floatingpoint because (1) he'd rather use those exponent bits for more precision, and (2) once you've done your numerical analysis you've already completed most of the work needed to represent your problem using fixedpoint arithmetic.
@betajet: ...once you've done your numerical analysis you've already completed most of the work needed to represent your problem using fixedpoint arithmetic.
LOL I think the main thing is to understand what one is trying to do and take the expected data and application into account. As you note, if you perform Y + X where Y is a very big value and X is a very small one, you will end up with just Y .... but if X and Y are both in the same ballpark sizewise, then the problem is much reduced.
Re: Decimal floating point...
TanjB
1/11/2014 2:32:48 AM
BetaJet, well yeah it would be nice to have a "proper analysis" but modern optimization software handles problems so huge (matrix dimensions millions of rows and columns) that noone really has a proper theory of what happens. As you say, FP is actually a set of fractional approximations and there are situations where severe loss of precision can occur. Observation suggests there are real world reasons why actual optimization problems routinely come close to singularity. In practice all commercial packages have black art tweaks to detect and recover.
One of my colleagues wrote an infinite (ulimited rationals) precision arithmetic package and we used that to get some insights and to check what the true optimal solutions were for some test cases. It was educational but too slow for real world use.
The field has changed enormously since JvN's time. Heck I think he died in that car crash before Simplex even became widespread. Numerical optimization theory blossomed in the 1980s with real insights into nonlinear, and then the implementations accelerated enormously in the 1990s and 2000s. Only the square root of the improvement due to hardware, the rest due to clever algorithms. I'm sure that John would love the kinds of optimization which we do today for monster problems like deep neural networks but it is a hugely different field than what he helped start.
Erroneous example
azassoko
1/8/2014 8:11:23 AM
Tom,
Sorry to disappoint you, but 101.011 = 5.375, not 5.15.
Sic transit gloria mundi...
AZ
Re: Erroneous example
tomii
1/8/2014 8:43:43 AM
@azassoko:
Sorry to disappoint you, but 101.011 = 5.375, not 5.15.
D'oh! Would you believe I'm no good at math?
Sic transit gloria mundi
And if this is how the world progresses, then we're in trouble... Oh, wait...
@Azassoko: Sic transit gloria mundi
"Obesa cantavit" (The fat lady has sung :)




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