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by teleforce·5y ago·view on hn ↗
Most of the methods I've mentioned apart from FIRRTL can be used for analog design. In fact, the JITX's product in particular only support ML-accelerated analog based design mainly circuit board level at the moment but nothing stopping them for doing it for both analog and digital design later on.

Don't get me wrong I'm not dismissing your work, I'm just dismissal of the outrages 1000x claim, as they say "extraordinary claims require extraordinary evidence"(ECREE). I believe Chris Lattner and Patrick Li will probably cringe to hear the 1000x claim ;-). Like I've mentioned before, I love to be proven wrong.

Regarding OpenBLAS, it will be very good to have native Julia alternative as you've claimed. It is nothing new to be better than OpenBLAS since D language has done it with probably less than 10x (100x?) man power compared to Julia several years back[1].

[1]http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/...

1 comments
The pure Julia (sub)BLAS (because they are incomplete right now) that benchmarks the best right now are Octavian and PaddedMatrices.jl. On Ryzen these BLAS's are doing extremely well:

https://github.com/JuliaLinearAlgebra/Octavian.jl/issues/24#...

but also on Intel:

https://chriselrod.github.io/PaddedMatrices.jl/dev/arches/ca...

I personally wouldn't spend too much time on BLAS-limited applications though, and this kind of circuit modeling is not one of them as I describe in another post. Also, it's 1000x at 99% accuracy: it's essentially a form of automated model order reduction which allows you to choose a tolerance and get more speedup matching the original circuit to the given tolerance.