VLSIDA
Generating SRAM gets stuck in extraction process of magic
Describe the bug Hello,
I'm trying to generating 2kByte Sram, but the process gets stuck in run_ext for nearly one day. This issue seems to occur more when sram size is large. I could make the whole sram generation process be able to finish by disabling lvs & commenting the stimulus and measurement part, but this is of course not a very good solution.
This is a similar issue like here->https://github.com/VLSIDA/OpenRAM/issues/245
And in the .ext.err file I find a lot of warnings, besides "unknown layers" and "boundary was redefined", there is also error shows "Error: Asymmetric device with multiple terminals!", which is also mentioned here->https://web.open-source-silicon.dev/t/424007/i-m-trying-the-sky130-support-in-openram-using-the-example-c
Version The current latest version
Expected behavior The process could finish without error
This sounds like a run time bug with magic. There isn't much we can do to speed up the extraction on the OpenRAM side.
My only questions are how much memory you have and are using. Especially if you are running it in Docker or a VM.
The asymmetric device message is expected since the SRAM cells have an asymmetric transistor or two.
This sounds like a run time bug with magic. There isn't much we can do to speed up the extraction on the OpenRAM side.
My only questions are how much memory you have and are using. Especially if you are running it in Docker or a VM.
Thanks for your quick reply. I'm running on Linux sever, it has around 124 GB memory available and another 37 GB for swap
This sounds like a run time bug with magic. There isn't much we can do to speed up the extraction on the OpenRAM side.这听起来像是 magic 的运行时错误。我们无能为力来加快 OpenRAM 端的提取速度。 My only questions are how much memory you have and are using. Especially if you are running it in Docker or a VM.我唯一的问题是你有多少内存和正在使用多少内存。特别是如果您在 Docker 或 VM 中运行它。
Thanks for your quick reply. I'm running on Linux sever, it has around 124 GB memory available and another 37 GB for swap感谢您的快速回复。我在 Linux 服务器上运行，它有大约 124 GB 的可用内存和另外 37 GB 的交换内存
Please how did you solve it in the end, I also ran into the problem in the running example
