Indeed, some examples:
replyhttps://news.ycombinator.com/item?id=12340348 Neural network spotted deep inside Samsung's Galaxy S7 silicon brain (2016)
https://ieeexplore.ieee.org/document/831066 Towards a high performance neural branch predictor (1999)
I didn't know that! Do you have any references that go into more depth here? I'd be curious how the architect and train it.
replyI believe D. A. Jimenez and C. Lin, "Dynamic branch prediction with perceptrons" is the paper which introduced the idea. It's been significantly refined since and I'm not too familiar with modern improvements, but B. Grayson et al., "Evolution of the Samsung Exynos CPU Microarchitecture" has a section on the branch predictor design which would talk about/reference some of those modern improvements.
replyThank you, I'll give them a read.
replyPerceptron? It's only linear prediction though
replyAt this point AI basically means "we didn't know how to solve the problem so we just threw a black box at it".
replyI disagree. More often than not is "We know how to solve the problem, and the solution is some linear algebra"
replyI disagree with both of you.
replyIt's not about linear algebra (which is just used as a way to represent arbitrary functions), it's about data. When your problem is better specified from data than from first principles, it's time to use an ML model.
I think what you're expressing is also known as "the Bitter Lesson".
replyOther news, is that HEP has used FPGAs for L0 triggers (amongst others) for decades. These always had a diverse selection criteria in their algorithms, event filters, suppression, weights etc.
And just mentioning, that some custom radhard simple readout silicon from the likes of STM isn't any news either.
replyAnd for historians: Delphi people (amongst others) had papers on Higgs selection using (A)NN from LEP data (overfit :) , obviously without the 5 sigma. It was an argument for LHC.
Dear downvoters/shadowbanners: do your homework.