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Stanford Seminar: Beyond Floating Point: Next Generation Computer Arithmetic

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  • Presented by John L. Gustafson, creator of unums
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32K views9 years ago6 recommendations

HardwareSystems programmingCPUs and computer architectureMath for programmersPerformance and optimization

John Gustafson introduces posits, a proposed drop-in replacement for IEEE 754 floating-point numbers designed to offer better accuracy and simpler hardware implementation.

EE380: Computer Systems Colloquium Seminar Beyond Floating Point: Next-Generation Computer Arithmetic Speaker: John L. Gustafson, National University of Singapore A new data type called a "posit" is designed for direct drop-in replacement for IEEE Standard 754 floats. Unlike unum arithmetic, posits do not require interval-type mathematics or variable size operands, and they round if an answer is inexact, much the way floats do. However, they provide compelling advantages over floats, including simpler hardware implementation that scales from as few as two-bit operands to thousands of bits. For any bit width, they have a larger dynamic range, higher accuracy, better closure under arithmetic operations, and simpler exception-handling. For example, posits never overflow to infinity or underflow to zero, and there is no "Not-a-Number" (NaN) value. Posits should take up less space to implement in silicon than an IEEE float of the same size. With fewer gate delays per operation as well as lower silicon footprint, the posit operations per second (POPS) supported by a chip can be significantly higher than the FLOPs using similar hardware resources. GPU accelerators, in particular, could do more arithmetic per watt and per dollar yet deliver superior answer quality. A series of comprehensive benchmarks compares how many decimals of accuracy can be produced for a set number of bits-per-value, using various number formats. Low-precision posits provide a better solution than "approximate computing" methods that try to tolerate decreases in answer quality. High-precision posits provide better answers (more correct decimals) than floats of the same size, suggesting that in some cases, a 32-bit posit may do a better job than a 64-bit float. In other words, posits beat floats at their own game. About the Speaker: Dr. John L. Gustafson is an applied physicist and mathematician. He is a former Director at Intel Labs and former Chief Product Architect at AMD. A pioneer in high-performance computing, he introduced cluster computing in 1985 and first demonstrated scalable massively parallel performance on real applications in 1988. This became known as Gustafson's Law, for which he won the inaugural ACM Gordon Bell Prize. He is also a recipient of the IEEE Computer Society's Golden Core Award. For more information about this seminar and its speaker, you can visit ee380.stanford.edu/Abstracts/170201.html Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum. Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week. Learn more: bit.ly/WinYX5

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6 people recommend this

What people say. Recommended as a fascinating introduction to posits and unums as potential replacements for standard floating-point arithmetic.

ngoldbaumon Lobsters • Recommends

Beyond Floating Point: Next Generation Computer Arithmetic This is a colloquium style talk which presents the idea of the "posit", a possible replacement for IEEE 754 floats. He starts talking about posits at the 15 minute mark.

espeedon Hacker News • Recommends

On “Why does e to pi i equal -1? (2015) [video]”

Have you seen "Stanford Seminar: Beyond Floating Point: Next Generation Computer Arithmetic"? link to this video

trsohmerson Hacker News • Recommends

On “The “high-level CPU” challenge (2008)”

Well unums in particular (especially the new type 3 "posits" ones) are very interesting and practical for hardware implementation. John Gustafson, the creator of unums, gave the first talk on type 3 unums at Stanford earlier this month, and there are already multiple hardware implementation efforts (one of which I am indirectly overseeing). While many people have recently hyped up low precision floating point for things like machine learning, Posits provide greater dynamic range AND greater accuracy for very few bits (as low as four, but with good ML starting 8 to 10 bits). On top of all of that, it costs less area and power in silicon than IEEE hardware. Check out the talk: link to this video

vanderZwanon Hacker News • Recommends

On “GCC optimized code gives strange floating point results”

I'm very surprised nobody has brought up John Gustafson's Unum work. His latest iteration is the posit, which he presented at Stanford earlier this month01 EDIT: apologies to CalChris2. What I should have said is discussed Gustafson's Unum work. 0 link to this video (relevant question at 1h22m37s) 1 link 2 link

dnauticson Hacker News • Recommends

On “Floating Point Visually Explained”

You may like: link to this video It turns out the math is slightly harder but it's faster than IEEE FP in hardware, probably because of less conditionals in the spec. There is better performance in terms of numerical accuracy at the cost of substantially more difficult error analysis. Disclaimer: I'm in the video.

gbrown_on Hacker News • Recommends

On “Floating-Point Determinism (2013)”

For anyone interested in this sort of thing you'd probably also be interested in John Gustafson's work on unums/posits. Here's a nice introductory presentation link to this video

u/GarkGarciain r/haskell • Mentions

Announcing posit: posit arithmetic in Haskell! Hi all! As a follow up to an old post of mine, I'd like to announce `posit`, a Cabal package that exposes bindings to the SoftPosit C library. For those of you unaware, posits are an alternative to IEEE floats, which provide better dynamic range and precision overall. The bindings are still under development. For now, the package exposes the `Numeric.Posit` module, which exports the `Posit8`, `Posit16` and `Posit32`/`Posit` types. All of those types implement `Eq`, `Ord`, `Num`, `Fractional` and `Storable`. I plan on adding support for quires and for the `posit_1_t` and `posit_2_t` types (whose meaning I haven't figured out yet). Feel free…

espeedon Hacker News • Mentions

On “Intel Gets Serious About Neuromorphic, Cognitive Computing Future”

Are these the Neuromorphic chips 1 Jeff Hawkins of Numenta 2 has been talking about? 1 Neuromorphic Chips link 2 Numenta papers/videos link It would also be good to see a major chip manufacturer or cloud provider that makes its own chips (Google/IBM) get serious about graph processing chips 3,4 and moving beyond floating point 5. 3 Novel Graph Processor Architecture link 4 Novel Graph Processor Architecture, Prototype System, and Results link 5 Stanford Seminar: Beyond Floating Point: Next Generation Computer Arithmetic link to this video

vanderZwanon Hacker News • Mentions

On “The Fundamental Theorem of Algebra: A Visual Approach (2015) [pdf]”

…ee sudden jumps from very bright to dark or the other way around). Then I realised: I have seen this in the wild! It's used all over the place in the demo-scene. For example: link link Aside, this also reminds me of Gustafson's closure plots for comparing his number encoding to standard floating points, although it's not really the same thing: link to this video…

dnauticson Hacker News • Mentions

On “How many floating-point numbers are in the interval [0,1]?”

so I'm the author of one of the first usable unum implementations. We're actually moving to something called sigmoid numbers which are even better. The lecture doesn't cover "valid mode" but that's more like the "unum". At the end of the video, there's a demonstration where I show some very interesting results concerning machine learning. link to this video You can try out sigmoid numbers in julia here: link Also, I am working on some verilog implementations, so we may have this in hardware rather soon. A few things about sigmoid numbers: 1) they have fixed sizes, so you don't have to worry about keeping track of things (which was a pain). 2) they have a property I call "isomorphic structure" - which…

Mindless2112on Hacker News • Mentions

On “16-bit math look-up tables – the unexpected power of scaled-integer math”

Unums are unlikely to gain much usage. Posits12, also by Gustafson, are a more reasonable alternative to IEEE-754 floating point (but will still have a difficult time displacing IEEE-754, if they can at all). 1 link 2 link to this video

_FKS_on Hacker News • Mentions

On “High Expectations for Low Precision at Cern”

Most likely related: John L. Gustafson presenting "Beyond Floating Point: Next Generation Computer Arithmetic" link to this video With papers here: - UNUMS: link - Posits: link

dnauticson Hacker News • Mentions

On “Matlab–Python–Julia Cheatsheet”

I should probably post evidence; this was a live demo I did of the floating point stuff at Stanford (demo begins ~53 minutes in) link to this video

dnauticson Hacker News • Mentions

On “JuliaCon2020: Julia Is Production Ready”

There was no paper, sorry, we talk about some of the research here link to this video big julia demo 40 minutes in. But the video doesn't feature the results I talk about (that happened a few months after the video) the specific results are somewhere in a DARPA presentation (but I don't have those slides, and the data are like 5 laptops back for me). The parallelism was, well, Gustafson parallelization, I pushed a randomly-generated 10k x 10k matrix solve to each of the nodes with a custom soft floating point to measure the relative numerical accuracy. Each node did all of one run per core, it was done very fast , but it would have taken months on a pc. As it was a gustafson scaling at its finest, ther…

dnauticson Hacker News • Mentions

On “Boeing to ground Starliner indefinitely until valve issue solved”

…r crazy short term things I predicted: Sarah Palin VP candidate, Feb 2008, Simone Giertz becoming famous (ca winter 2015). I also predicted the failure of the first starliner launch (documented here in HN). I also have managed to pick some ascendant "obscure" PLs just before hockey sticking. Used Julia professionally in 2015 (proof of use in 2017: link to this video ), used Elixir in 2017, been using Zig since 2019. And I haven't really used any other non-mainstream PLs, so I have a pretty good hit record I think. And a co…