ScaleHLS is a High-level Synthesis (HLS) framework on MLIR. ScaleHLS can compile HLS C/C++ or PyTorch model to optimized HLS C/C++ in order to generate high-efficiency RTL design using downstream tools, such as Vivado HLS.
By using the MLIR framework that can be better tuned to particular algorithms at different representation levels, ScaleHLS is more scalable and customizable towards various applications coming with intrinsic structural or functional hierarchies. ScaleHLS represents HLS designs at multiple levels of abstraction and provides an HLS-dedicated analysis and transform library (in both C++ and Python) to solve the optimization problems at the suitable representation levels. Using this library, we've developed a design space exploration engine to generate optimized HLS designs automatically.
For more details, please see our HPCA'22 paper:
@article{ye2021scalehls,
title={ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation},
author={Ye, Hanchen and Hao, Cong and Cheng, Jianyi and Jeong, Hyunmin and Huang, Jack and Neuendorffer, Stephen and Chen, Deming},
journal={arXiv preprint arXiv:2107.11673},
year={2021}
}
- cmake
- ninja
- clang and lld (recommended)
- pybind11
- python3 with numpy
$ git clone --recursive git@github.com:hanchenye/scalehls.git
$ cd scalehls
Run the following script to build ScaleHLS. Note that you can use -j xx
to specify the number of parallel linking jobs.
$ ./build-scalehls.sh
After the build, we suggest to export the following paths.
$ export PATH=$PATH:$PWD/build/bin:$PWD/polygeist/build/bin
$ export PYTHONPATH=$PYTHONPATH:$PWD/build/tools/scalehls/python_packages/scalehls_core
To launch the automatic kernel-level design space exploration, run:
$ cd samples/polybench/gemm
$ mlir-clang test_gemm.c -function=test_gemm -memref-fullrank -raise-scf-to-affine -S \
| scalehls-opt -dse="top-func=test_gemm target-spec=../config.json" -debug-only=scalehls \
| scalehls-translate -emit-hlscpp > test_gemm_dse.cpp
Meanwhile, we provide a pyscalehls
tool to showcase the scalehls
Python library:
$ pyscalehls.py test_gemm.c -f test_gemm > test_gemm_pyscalehls.cpp
If you have installed Torch-MLIR, you should be able to run the following test:
$ cd samples/pytorch/resnet18
$ # Parse PyTorch model to TOSA dialect (with mlir_venv activated).
$ # This may take several minutes to compile due to the large amount of weights.
$ python3 export_resnet18_mlir.py | torch-mlir-opt \
-torchscript-module-to-torch-backend-pipeline="optimize=true" \
-torch-backend-to-tosa-backend-pipeline="optimize=true" > resnet18.mlir
$ # Optimize the model and emit C++ code.
$ scalehls-opt resnet18.mlir \
-scalehls-pytorch-pipeline="top-func=forward opt-level=2" \
| scalehls-translate -emit-hlscpp > resnet18.cpp
The project follows the conventions of typical MLIR-based projects:
include/scalehls
andlib
for C++ MLIR compiler dialects/passes.polygeist
for the HLS C/C++ front-end.samples
for C/C++ and PyTorch examples.test
for holding regression tests.tools
for command line tools, such asscalehls-opt
andpyscalehls
.