circt VS Modia.jl

MLIR (https://mlir.llvm.org/) is a quickly growing compiler toolkit which attempts to synthesize the learnings of LLVM and currently powers compilers for programming languages, machine learning and circuit design (https://github.com/llvm/circt). and there are a ton of companies with real employees working on it (including Microsoft) and MLIR is at the core of Chris Lattner’s new company, ModularAI. I’d recommend taking a look at it, there are a large number of ways to get involved and a number of paths from contributor to employee.

Might be good to target the CIRCT infrastructure at some point.

Even small optimizations like removing unused pins from internal modules are often times opposed.

Chris Lattner and others are currently working on an "industry" version of firrtl as part of the CIRCT hardware compiler framework: https://github.com/llvm/circt

Did you see the work being done on CIRCT? https://github.com/llvm/circt

I remember one of the reasons you did not want to use firrtl was that its compiler is implemented in Scala and thus hard to integrate into other projexts. CIRCT will solve that problem by providing a firrtl compiler implemented in C++. Other languages like Verilog/VHDL and new high level languages for HLS-like designs are also on the todo list.

Relevant: https://github.com/llvm/circt

For circuits a lot of them are represented by differential-algebraic equations (DAEs) and require modeling tools in order to handle the high differential index of the systems. This is the reason why they are typically handled via acausal modeling systems which can do index reduction. For Julia, this is the ModelingToolkit portion of the SciML ecosystem (https://docs.sciml.ai/ModelingToolkit/stable/), and some modeling tools like https://github.com/ModiaSim/Modia.jl and OpenModelica front-ends https://github.com/OpenModelica/OMJulia.jl.

The thing to watch in the space of Simulink/Modelica is https://github.com/SciML/ModelingToolkit.jl . It's an acausal modeling system similar to Modelica (though extended to things like SDEs, PDEs, and nonlinear optimization), and has a standard library (https://github.com/SciML/ModelingToolkitStandardLibrary.jl) similar to the MSL. There's still a lot to do, but it's pretty functional at this point. The two other projects to watch are FunctionalModels.jl (https://github.com/tshort/FunctionalModels.jl, which is the renamed Sims.jl), which is built using ModelingToolkit.jl and puts a more functional interface on it. Then there's Modia.jl (https://github.com/ModiaSim/Modia.jl) which had a complete rewrite not too long ago, and in its new form it's fairly similar to ModelingToolkit.jl and the differences are more in the details. For causal modeling similar to Simulink, there's Causal.jl (https://github.com/zekeriyasari/Causal.jl) which is fairly feature-complete, though I think a lot of people these days are going towards acausal modeling instead so flipping Simulink -> acausal, and in that transition picking up Julia, is what I think is the most likely direction (and given MTK has gotten 40,000 downloads in the last year, I think there's good data backing it up).

And quick mention to bring it back to the main thread here, the DataDrivenDiffEq symbolic regression API gives back Symbolics.jl/ModelingToolkit.jl objects, meaning that the learned equations can be put directly into the simulation tools or composed with other physical models. We're really trying to marry this process modeling and engineering world with these "newer" AI tools.

Maybe of interest in that context:

https://github.com/ModiaSim/Modia.jl

The authors of that tool have a strong background in modeling and simulation of differential algebraic equations. Not so much in designing DSLs, though, so there maybe some technical oddities. But I expect the simulation aspect to be quite decent.