AI has pushed an explosion of latest quantity codecs—the methods through which numbers are represented digitally. Engineers are taking a look at each potential approach to save computation time and energy, together with shortening the variety of bits used to symbolize information. However what works for AI doesn’t essentially work for scientific computing, be it for computational physics, biology, fluid dynamics, or engineering simulations. IEEE Spectrum spoke with Laslo Hunhold, who lately joined Barcelona-based Openchip as an AI engineer, about his efforts to develop a bespoke quantity format for scientific computing.
LASLO HUNHOLD
Laslo Hunhold is a senior AI accelerator engineer at Barcelona-based startup Openchip. He lately accomplished a Ph.D. in laptop science from the College of Cologne, in Germany.
What makes quantity codecs fascinating to you?
Laslo Hunhold: I don’t know one other instance of a subject that so few are focused on however has such a excessive impression. If you happen to make a quantity format that’s 10 p.c extra [energy] environment friendly, it may possibly translate to all functions being 10 p.c extra environment friendly, and it can save you lots of power.
Why are there so many new quantity codecs?
Hunhold: For many years, laptop customers had it very easy. They might simply purchase new methods each few years, and they might have efficiency advantages at no cost. However this hasn’t been the case for the final 10 years. In computer systems, you may have a sure variety of bits used to symbolize a single quantity, and for years the default was 64 bits. And for AI, corporations observed that they don’t want 64 bits for every quantity. So they’d a robust incentive to go right down to 16, 8, and even 2 bits [to save energy]. The issue is, the dominating commonplace for representing numbers in 64 bits just isn’t effectively designed for decrease bit counts. So within the AI subject, they got here up with new codecs that are extra tailor-made towards AI.
Why does AI want totally different quantity codecs than scientific computing?
Hunhold: Scientific computing wants excessive dynamic vary: You want very massive numbers, or very small numbers, and really excessive accuracy in each circumstances. The 64-bit commonplace has an extreme dynamic vary, and it’s many extra bits than you want more often than not. It’s totally different with AI. The numbers often comply with a selected distribution, and also you don’t want as a lot accuracy.
What makes a quantity format “good”?
Hunhold: You may have infinite numbers however solely finite bit representations. So you have to determine the way you assign numbers. An important half is to symbolize numbers that you simply’re really going to make use of. As a result of in case you symbolize a quantity that you simply don’t use, you’ve wasted a illustration. The only factor to have a look at is the dynamic vary. The following is distribution: How do you assign your bits to sure values? Do you may have a uniform distribution, or one thing else? There are infinite prospects.
What motivated you to introduce the takum quantity format?
Hunhold: Takums are primarily based on posits. In posits, the numbers that get used extra continuously may be represented with extra density. However posits don’t work for scientific computing, and this can be a big challenge. They’ve a excessive density for [numbers close to one], which is nice for AI, however the density falls off sharply when you have a look at bigger or smaller values. Folks have been proposing dozens of quantity codecs in the previous few years, however takums are the one quantity format that’s really tailor-made for scientific computing. I discovered the dynamic vary of values you utilize in scientific computations, in case you have a look at all of the fields, and designed takums such that whenever you take away bits, you don’t cut back that dynamic vary
This text seems within the March 2026 print challenge as “Laslo Hunhold.”
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