1. Because you will piss off would-be users. That simple. I have also complained about this endlessly. I also despise Tcl and had to use it to help automate lots of EDA tools in ASIC and FPGA designs, so I'm with you on this. But it isn't a hard question to answer in reality. Users will just be annoyed and think you are annoying and contrarian. (Nextpnr at least allows scripting with Python, and I'd ideally like to see other takes on synthesis in the next few years, thanks to more robust language frontends coming along.)
1.1. It is not "just scripting ABC", but ignoring that. I assume it's because Claire just liked C++. She was historically an embedded/hardware engineer. A lot of them like C++. But I can't read anyone's mind. The project is like, probably 15 years old now (the initial Git checkins from 2013 aren't even full replications of the real history, to my knowledge, which go further back into an ancient SVN server.) What would you have suggested using in 2010? Python with zero static typing and pip?
2. Because actually owning the code you write and ship to your users is a nice and good thing. Dreamplace is a DL-reinforced placer. It's an academic project. Having rode that bull (shipping academic code to production users and then having to rework it again and again), realistically, I'd probably reimplement Dreamplace directly into the codebase if I wanted to ship features to users. No offense but this isn't a SaaS service where the sausage machine is hidden; and most RTL engineers, nor I, want to install some giant annoying shitware ML stack into a python virtualenv to run Nextpnr. Just to be clear, if yosys uses some magical ML reinforcement learning, or doesn't, I don't care. It would be great if it did and it got a bajillion % higher QoR. But I don't want actually using the thing to be a giant pain in the ass.
Most open source software engineering isn't about being Super Epic. It's about meeting users where they are at and handling basic engineering constraints around the project. EDA is no different.
I'm not downplaying the fact that yosys is a logic synthesizer - it's valuable and it's the only one of its kind. But that doesn't change the fact that many of the steps are just piping ABC or nextpnr or whatever and doing it using cpp. See https://github.com/search?q=repo%3AYosysHQ%2Fyosys%20ScriptP.... And like I know this not tangentially but because I tried use yosys but not all of yosys and it was impossible to decompose it.
> No offense but this isn't a SaaS service where the sausage machine is hidden; and most RTL engineers, nor I, want to install some giant annoying shitware ML stack into a python virtualenv to run Nextpnr. Just to be clear, if yosys uses some magical ML reinforcement learning, or doesn't, I don't care. It would be great if it did and it got a bajillion % higher QoR. But I don't want actually using the thing to be a giant pain in the ass.
I'm with you on every instance of this trueism except here, where runtime of the tool is not just important it's absolutely critical for DSE.
> Most open source software engineering isn't about being Super Epic
Again I agree with you in every other instance except here - just like LLVM and gcc can't be just oss, they need to be nuclear weapons grade software in order to have won mindshare (and investment) away from commercial compilers so does yosys.
So you can take me to task for being naive and arrogant but the reality is that the decisions made by yosys prevent further serious investment by anyone that's not playing with toys.
1.1. It is not "just scripting ABC", but ignoring that. I assume it's because Claire just liked C++. She was historically an embedded/hardware engineer. A lot of them like C++. But I can't read anyone's mind. The project is like, probably 15 years old now (the initial Git checkins from 2013 aren't even full replications of the real history, to my knowledge, which go further back into an ancient SVN server.) What would you have suggested using in 2010? Python with zero static typing and pip?
2. Because actually owning the code you write and ship to your users is a nice and good thing. Dreamplace is a DL-reinforced placer. It's an academic project. Having rode that bull (shipping academic code to production users and then having to rework it again and again), realistically, I'd probably reimplement Dreamplace directly into the codebase if I wanted to ship features to users. No offense but this isn't a SaaS service where the sausage machine is hidden; and most RTL engineers, nor I, want to install some giant annoying shitware ML stack into a python virtualenv to run Nextpnr. Just to be clear, if yosys uses some magical ML reinforcement learning, or doesn't, I don't care. It would be great if it did and it got a bajillion % higher QoR. But I don't want actually using the thing to be a giant pain in the ass.
Most open source software engineering isn't about being Super Epic. It's about meeting users where they are at and handling basic engineering constraints around the project. EDA is no different.