Note
Starting July 11, 2024 we will be offering (monthly) Seal5 Development/User meetings. Click here for details, if you are interested!
The RISC-V instruction set architecture (ISA) is popular for its extensibility. However, a quick exploration of instruction candidates fails due to the lack of tools to auto-generate embedded software toolchain support. Seal5 work establishes a semi-automated flow to generate LLVM compiler support for custom instructions based on the CoreDSL2 ISA description language. Seal5 is capable of generating support for functionalities ranging from baseline assembler-level support, over builtin functions to compiler code generation patterns for scalar as well as vector instructions, while requiring no deeper compiler know-how.
Eliminating manual efforts for Retargeting is crutial for the automated exploration of custom RISC-V instructions as depicted in the following image. Seal5's code-generation support allows to make use of custom instructions without needing to make changes to the programs/benchmarks source code (i.e. adding inline-assembly calls).
To be able to run the examples, make sure to clone the Seal5 repo using the --recursive
flag. Otherwise run the following command to fetch (and update) the referenced submodules.
git submodule update --init --recursive
First, a set of APT packages needs to be installed:
sudo apt install python3-pip python3-venv cmake make ninja-build
First, setup a virtual environment with Python v3.8 or newer.
Install all required python packages using the follow8n* command:
pip install -r requirements.txt
.
For development (linting, packaging,...) there are a few more dependencies which can be installed via:
pip install -r requirements_dev.txt
.
Warning: It is highly recommended to install seal5
into a new virtual environment. Follow these steps to initialize and enter a venv in your seal5 repo directory:
# alternative: python3 -m venv venv
virtualenv -p python3.8 venv
source venv/bin/activate
pip install seal5
First prepare your shell by executing export PYTHONPATH=$(pwd):$PYTHONPATH
inside the seal5 repository. Then you should be able to use Seal5 without needing to reinstall it.
Alternatively you should be able to use pip install -e .
.
The flow can be sketched as follows (see Example below for functional code!):
# Create flow
seal5_flow = Seal5Flow(...)
# Initialize LLVM repo and .seal5 directories
seal5_flow.initialize(...)
# Optional: remove artifacts from previous builds
seal5_flow.reset(...)
# Install Seal5 dependencies (CDSL2LLVM/PatternGen)
seal5_flow.setup(...)
# Load CoreDSL2+CFG files (Git config, filters,...)
seal5_flow.load(...)
# Transform Seal5 model (Extract side effects, operands,...)
seal5_flow.transform(...)
# Generate patches based on Seal5 model (ISel patterns, RISC-V features,...)
seal5_flow.generate(...)
# Apply generated (and manual) patches to LLVM codebase
seal5_flow.patch(...)
# Build patches LLVM (This will take a while)
seal5_flow.build(...)
# Run LLVM+Seal5 tests to verify functionality
seal5_flow.test(...)
# Combine patches and install LLVM
seal5_flow.deploy(...)
# Archive final LLVM (optionally inclusing logs, reports,...)
seal5_flow.export(...)
# Optional: Cleanup all artifacts
seal5_flow.cleanup(...)
Command line interface is aligned with the Python API. See examples/demo.sh
for an full usage example
export SEAL5_HOME=...
seal5 --dir $SEAL5_HOME reset --settings
seal5 init [--non-interactive] [-c]
seal5 load --files ...
seal5 setup ...
seal5 transform ...
seal5 generate ...
seal5 patch ...
seal5 build ...
seal5 test ...
seal5 deploy ...
seal5 export ...
seal5 clean [--temp] [--patches] [--models] [--inputs]
See examples/demo.py
for example of end-to-end flow!
Checkout Seal5's ReadTheDocs Page!
See here.
We added a (manual) CI job to run the examples/demo.py
script via GitHub actions.
Seal5 issue tracker: https://github.com/tum-ei-eda/seal5/issues
CoreDSL2LLVM/PatternGen issue tracker: https://github.com/mathis-s/CoreDSL2LLVM/issues
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This research is partially funded by the German Federal Ministry of Education and Research (BMBF) within the projects Scale4Edge (grant number 16ME0465) and MANNHEIM-FlexKI (grant number 01IS22086L).