All real numbers are representable, but not necessarily to arbitrary precision.

You should really read the book. A lot of the atomic operations seem cumbersome, because the unum doesn't have a fixed size representation, but you just get over that when you remember that the real problem is shuttling data over buses. More compute transistors is not a problem.

If I had a number system of -100, 0, and 100, could I claim all real numbers are represented, just not to arbitrary precision? With n bits, there are 2^n possible representations max. If you can say all real numbers are represented for n around 30, or 60, or even 1,000,000, then can't you say the same for n equal to 1 or 2?

Now I think we can say some representations are better than others (at least for certain applications). It may even be possible that some representation with a given n can be better in all cases than a representation with a larger n. So an n equals 1 system will obviously be terrible compared to many systems with n around 30 or 60. But I'm not really concerned with that point, just the claim of being able to represent all real numbers.

Sure, you can claim that but it doesn't mean squat unless your underlying circuits correctly process operations (*,+,/etc) on the representation in a way that is faithful to your claim.

In the book he talks about a simple unum with values -inf, less than -2, -2, between -2 ... -1, -1, between -1 ... 0, 0, 0...1, 1, 1...2, 2, > 2, and inf, also intervals like 0...2, -2...0 etc 1...inf, emerge when you intentionally use less precision.

This is a mathematically closed system that represents all real numbers and is even useful!

No. The set of "representable" (i.e. computable) numbers is countable (because the set of programs is countable), so in fact almost all real numbers cannot be represented.

I got the impression that the unum explicitly modeled a range of values, so I'm not sure that applies.

Not an expert here though.

Regardless if it is representing number, ranges, or a mix of both, there are still 2^n maximum possible representations for n bits. Perhaps you could create a mix of numbers and ranges equal to (2^n)-2 and then add one range that is from the lowest number represented to negative infinity and add another that does the same for positives. But I can do that system with -, 0, +, NAN. See, I just represented all numbers. It isn't as useful, but boy are there savings on storage, cost, and computation time.

Unums shouldn't be seen as better than ints, longs, floats, or doubles anymore than a double is seen as better than an int. They have different uses and strengths, and there are problems where none are a good choice. Perhaps unums are the best choice for some problems. Perhaps they are clearly and significantly better in some areas. But they should be thought of as an alternative to the existing formats and not a superior replacement.

I would argue that unums are a "superior replacement" for doubles in many cases, though: in the case that you support unums that are "wide" enough, you can represent doubles exactly, plus you have additional values, plus some nice rules about when approximation error occurs/is propagated and not as many bits need to be stored or moved around on buses. It'll be a while before there's an implementation anywhere near as fast as existing FPUs, but Gustafson makes a good argument for his format. Personally, I'm more interested in the correctness benefits than the space/power/time savings - even if unums are never faster than 64-bit floats, they present an interesting way to do real-number arithmetic and my brief exposure to them leaves me much more confident that I could write numerical algorithms correctly than with doubles - I do numerical/statistical algorithms with doubles in my work, and it's really a pain to reason about things that the "uncertain" (open-inverval) values of the unum format would greatly simplify.

They're also an inferior replacement in the case that you want to take advantage of highly-optimized hardware, and that getting a correct answer doesn't really matter. I don't see unums replacing floats for, say, video game graphics. But for numerical computation, it seems like the only real flaw with unums compared to doubles is the nonexistence of a hardware implementation, and the existing popularity of doubles.

Agreed. For neural networks. I would argue the opposite is true, you should just have a 16bit float that casts really large values to infinity silently without throwing errors, with a logistic lookup that maps "inf" to +/- 1... A mathematically incorrect float is operationally superior to the correct one.

Bonus points if values near 0 are treated as 0 (encourages sparsity!)

For hardware without a FPU, you can do floats in software when accuracy is more important than speed (vs fixed point), one can imagine that if this thing actually works then for hardware without a "UPU" one can just do unums in software when needed.

That's fine, but then you're representing dyadic intervals, not real numbers. Simple question: what's unum[sqrt(2)]?

Rougly speaking It would be 1.414... + plus a bit that indicates the result is between two adjacent exact values. This bit would not be set if the result were exact.

It's not the same as dyadic interval math, it's kind of a hybrid.

And you could set the environmental variables to be as precise as you need. If you need 29 digits of precision, you can quickly show that you know 1.4142135623730950488016887242 and nothing more.

A reasonable metaphor would be that you cannot draw all of the Mandelbrot diagram, but you can render any piece of it to any resolution you choose. Being able to describe it precisely, and to know the limits of your accuracy, is useful.

I am not sure that I understand what you're saying. Given a charitable reading of the presentation, the author seems to be saying that his standard explicitly specifies range of values, as opposed to the IEEE Float. It did not seem like he was saying that he could explicitly represent an infinite amount of exact, distinct values using a finite number of bits. Low level data structures are not my area of expertise, so did I misunderstand what he was saying or what you are saying?

Under that reading, he's saying nothing: just represent the interval [-inf, inf] as "0" and call it a day.

So assuming that he's saying anything at all, he's at least being imprecise, and the actually claim should be something like "represent any dyadic interval using a finite number of bits".

He's a bit showy about the format. Wish he would just put out a technical paper.

Anyway, I guess his motivation might be "you can represent any real number (with finite bits, and therefore finite precision)". In the book, he presents an interesting case: little 4-bit versions of the Unum that can represent:

-inf, (-inf, -2), -2, (-2, -1), (-1, -1/2), -1/2, (-1/2, -0), -0, 0, (0, 1/2), 1/2, (1/2, 1), 1, (1, 2), 2, (2, inf), inf.

Putting together a pair of them, the book outlines simple interval arithmetic (where pairs of numbers can represent any interval between numbers on the line above, and single numbers can represent some of the closed intervals as above). The reason these are kind of neat is that using the standard Unum algorithms (without any fudging), you can get "correct" (albeit terribly imprecise) results for many real number computations. Questions like "is there a number satisfying a numerical predicate in some range?" or the value of a trigonmetric or exponential expression will come out "correct" (but you might get an answer like (-inf, inf)). If things work out as well as he claims (and demonstrates for some cases), then you can basically do the math to figure out how precise you want to be and choose an appropriate specialization of the format - or take advantage of the format's flexibility and do computations starting at a low precision and increasing precision until you are satisfied. In particular, it's kind of cool that you can do computations with little 8-bit intervals, and possibly circumvent doing more expensive computations (e.g. if you test if a property will hold anywhere in the Unum range and it won't hold anywhere, assuming you (and Gustafson) have done the math right, you can avoid doing more expensive checks with increased precision).

Anyway, point is, the presentations are kind of flashy and misleading - and you're right, you can't represent any real number (just finitely representable dyadic intervals)... but the format itself _does_ seem promising...

If I add (1/2, 1) to (1, 2), then I should get (3/2,3), which is not representable with any of the above values. So what is the result and in what sense is it correct?

As I understand it, it's just "floating" interval arithmetic, so like in interval arithmetic you should understand a representation [a,b] as 'The number is contained in the interval [a,b]'. In other words, interval arithmetic with carefully assigned floating point limits such those don't violate the interval arithmetic and are concise.

Not sure how it would go, I think it depends if you maintain the precision or increase it after this operation. I guess 2 bit: (1/2,1)+(1,2)=(1,inf) (?) and 4 bit (2 exp and 2 mantissa?): (1/2,1)+(1,2)=(3/2,3). Maybe there's a built in check to compare how short your interval can get and stop at a reasonable precision (in this case there's no point going for more than 4 bits).

Honestly I find it quite elegant and is at least trying to solve a big issue with bandwidth limitations. I do wish the exposition was more clear/straightforward.

I think the correct answer is (0,inf) which is a value you can get by dropping to a lower resolution within the same unum environment.

Ah, that makes sense. The 4-bit representations do not suffice by themselves, but imply the support of some or all of the smaller representations as well, and I guess you always need a representation for [-inf,inf].

First of all, a correction: the 4-bit unums can represent any of

-inf, (-inf, -2), -2, (-2, -1), (-1, 0), 0, (0, 1), (1, 2), 2, (2, inf), inf, and both quiet and signaling NaN. They do not represent ±1/2 or use ±1/2 as an endpoint.

If you add, say, 1 to (1,2), you get (2, inf). The open interval means it does not CONTAIN infinity, but ends at a finite value too large to represent. If the largest positive real you can represent is 2, then (2, inf) is a mathematically correct answer.

D'oh, missed the NaNs and wrote out a list without referring to the book... then checked the length of the list to make sure it had 8 things. Silly me!

Your list should include -1 and 1 as well, shouldn’t it?

Edit: here was the slide http://i.imgur.com/0eQvvK1.png

Those would be among the 3-bit unums.