AI has pushed an explosion of latest quantity codecs—the methods through which numbers are represented digitally. Engineers are taking a look at each attainable means 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 pc science from the College of Cologne, in Germany.
What makes quantity codecs attention-grabbing to you?
Laslo Hunhold: I don’t know one other instance of a subject that so few are concerned with however has such a excessive affect. For those who make a quantity format that’s 10 % extra [energy] environment friendly, it may well translate to all purposes being 10 % extra environment friendly, and it can save you a number of power.
Why are there so many new quantity codecs?
Hunhold: For many years, pc customers had it very easy. They may simply purchase new programs each few years, and they’d have efficiency advantages without cost. However this hasn’t been the case for the final 10 years. In computer systems, you will have a sure variety of bits used to symbolize a single quantity, and for years the default was 64 bits. And for AI, firms seen 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 will not be properly 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 completely 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 completely different with AI. The numbers normally observe a particular distribution, and also you don’t want as a lot accuracy.
What makes a quantity format “good”?
Hunhold: You will have infinite numbers however solely finite bit representations. So you’ll want to determine the way you assign numbers. An important half is to symbolize numbers that you simply’re truly going to make use of. As a result of when you symbolize a quantity that you simply don’t use, you’ve wasted a illustration. The best 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 will have a uniform distribution, or one thing else? There are infinite potentialities.
What motivated you to introduce the takum quantity format?
Hunhold: Takums are primarily based on posits. In posits, the numbers that get used extra incessantly will be represented with extra density. However posits don’t work for scientific computing, and this can be a enormous difficulty. 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 truly tailor-made for scientific computing. I discovered the dynamic vary of values you utilize in scientific computations, when you have a look at all of the fields, and designed takums such that whenever you take away bits, you don’t scale back that dynamic vary
This text seems within the March 2026 print difficulty as “Laslo Hunhold.”
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