Tiny Classifier Circuits: Evolving Accelerators for Tabular Data

by PaulHoule • 3 years ago

40 12 3 years ago

That's really interesting. Skimmed the paper really quickly, definitely need to do a deeper dive into the paper.

From the Abstract:

Despite Tiny Classifiers being constrained to a few hundred logic gates, we observe no statistically significant difference in prediction performance in comparison to the best-performing ML baseline. When synthesised as a Silicon chip, Tiny Classifiers use 8-56x less area and 4-22x less power. When implemented as an ultra-low cost chip on a flexible substrate (i.e., FlexIC), they occupy 10-75x less area and consume 13-75x less power compared to the most hardware-efficient ML baseline. On an FPGA, Tiny Classifiers consume 3-11x fewer resources.

Seems like this would be a pretty significant result if true.

mock-possum• 3 years ago

Wonder what the catch is

detrites• 3 years ago

Catch seems to be no detailed implementation/code as it's a private company and so likely a proprietary technology.

Haven't read the whole paper and would be delighted to be wrong.

Faint• 3 years ago

That you have to have at least FPGA to run it?

dimatura2• 3 years ago

Only skimmed the paper, but I'd say one potential issue is that they show good results in tabular data domains, where it's likely easier to get away with minimal models than in other domains, such as visual/lidar/audio data. And it seems to me those are the domains where it'd actually be more interesting to deploy this kind of model. In either case, there seems to be a tradeoff in that while you're saving space/energy with the hardware model, updating that model after its deployed might be more complicated than with a typical software-only model.

UncleOxidant3• 3 years ago

I think that catch is that these are going to be fairly small classification problems and that they've reported the results where it worked well and we're not seeing the ones where it didn't?

Tiny Classifier Circuits: Evolving Accelerators for Tabular Data

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