diff --git a/README.md b/README.md index 6403cc7..9157660 100644 --- a/README.md +++ b/README.md @@ -1,109 +1,7 @@ -# About -The Timeloop Front-End (TimeloopFE) is a Python front-end interface to the -Timeloop infrastructure, which allows users to model tensor accelerators and -explore the vast space of architectures, workloads, and mappings. +# Moved to Pytimeloop +Timeloopfe has been moved and is now part of the [Pytimeloop +project](https://github.com/Accelergy-Project/timeloop-python/tree/main). +Pytimeloop includes both Timeloopfe and a ton of new features and improvements-- +check it out! -TimeloopFE provides a rich Python interface, error checking, and automation -tools. With closely-aligned Python and YAML interfaces, TimeloopFE is designed -to enable easy design space exploration and automation. - -## Documentation -Documentation for the full framework is available at -[timeloop.csail.mit.edu](https://timeloop.csail.mit.edu). Documentation for TimeloopFE -is available at -[accelergy-project.github.io/timeloopfe/index.html](https://accelergy-project.github.io/timeloopfe/index.html). - -## Installation -First, ensure that Timeloop and Accelergy are installed following the -[Timeloop+Accelergy install instructions](https://timeloop.csail.mit.edu/installation). - -To install timeloopfe, run the following commands: -```bash -git clone -https://github.com/Accelergy-Project/timeloopfe.git -pip3 install ./timeloopfe -``` - -## Tutorials and Examples -Tutorials and examples available in the [Timeloop and Accelergy exercises -repository](https://github.com/Accelergy-Project/timeloop-accelergy-exercises.git). -In this repository, examples can be found in the `workspace/baseline_designs` -directory and tutorials can be found in the `workspace/exercises` directory. - -## Minimal Usage -TimeloopFE interface provides two primary functions: - Input file gathering & -error checking - Python interface for design space exploration -```python -import timeloopfe.v4 as tl -from joblib import Parallel, delayed - -# Basic setup. Gathers input files, checks for errors -spec = tl.Specification.from_yaml_files( - "your_input_file.yaml", "your_other_input_file.yaml" -) -# Call Timeloop mapper -tl.call_mapper(spec, output_dir="your_output_dir") -# Call Accelergy verbose -tl.call_accelergy_verbose(spec, output_dir="your_output_dir") - -# Multiprocessed design space exploration -def run_mapper_with_spec(buf_size: int): - spec = tl.Specification.from_yaml_files( - "your_input_file.yaml", "your_other_input_file.yaml" - ) - spec.architecture.find("my_buffer").attributes.depth = buf_size - return tl.call_mapper(spec, output_dir=f"outputs_bufsize={buf_size}") - -buf_sizes = [1024, 2048, 4096, 8192, 16384] -results = Parallel(n_jobs=8)( - delayed(run_mapper_with_spec)(buf_size) for buf_size in buf_sizes -) -``` - -Please visit the [Timeloop and Accelergy exercises -repository](https://github.com/Accelergy-Project/timeloop-accelergy-exercises.git) -for more examples and tutorials. - -## Citation -Please cite the following: - -- A. Parashar, P. Raina, Y. S. Shao, Y.-H. Chen, V. A. Ying, A. Mukkara, R. Venkatesan, B. Khailany, S. W. Keckler, and J. Emer, “Timeloop: A systematic approach to DNN accelerator evaluation,” in 2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2019, pp. 304–315. -- M. Horeni, P. Taheri, P. Tsai, A. Parashar, J. Emer, and S. Joshi, “Ruby: Improving hardware efficiency for tensor algebra accelerators through imperfect factorization,” in 2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2022, pp. 254–266. -- Y. N. Wu, P.-A. Tsai, A. Parashar, V. Sze, and J. S. Emer, “Sparseloop: An analytical, energy-focused design space exploration methodology for sparse tensor accelerators,” in 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2021, pp. 232–234. -- Y. N. Wu, J. S. Emer, and V. Sze, “Accelergy: An architecture-level energy estimation methodology for accelerator designs,” in 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2019, pp. 1–8. -- T. Andrulis, J. S. Emer, and V. Sze, “CiMLoop: A flexible, accurate, and fast compute-in-memory modeling tool,” in 2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2024. - -Or use the following BibTeX: - -```BibTeX -@inproceedings{timeloop, - author = {Parashar, Angshuman and Raina, Priyanka and Shao, Yakun Sophia and Chen, Yu-Hsin and Ying, Victor A and Mukkara, Anurag and Venkatesan, Rangharajan and Khailany, Brucek and Keckler, Stephen W and Emer, Joel}, - booktitle = {2019 IEEE international symposium on performance analysis of systems and software (ISPASS)}, pages={304--315}, year={2019}, - title = {Timeloop: A systematic approach to dnn accelerator evaluation}, - year = {2019}, -} -@inproceedings{ruby, - author = {M. Horeni and P. Taheri and P. Tsai and A. Parashar and J. Emer and S. Joshi}, - booktitle = {2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)}, - title = {Ruby: Improving Hardware Efficiency for Tensor Algebra Accelerators Through Imperfect Factorization}, - year = {2022}, -} -@inproceedings{sparseloop, - author = {Wu, Yannan N. and Tsai, Po-An, and Parashar, Angshuman and Sze, Vivienne and Emer, Joel S.}, - booktitle = {{ ACM/IEEE International Symposium on Microarchitecture (MICRO)}}, - title = {{Sparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling }}, - year = {{2022}} -} -@inproceedings{accelergy, - author = {Wu, Yannan Nellie and Emer, Joel S and Sze, Vivienne}, - booktitle = {2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)}, - title = {Accelergy: An architecture-level energy estimation methodology for accelerator designs}, - year = {2019}, -} -@inproceedings{cimloop, - author = {Andrulis, Tanner and Emer, Joel S. and Sze, Vivienne}, - booktitle = {2024 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)}, - title = {{CiMLoop}: A Flexible, Accurate, and Fast Compute-In-Memory Modeling Tool}, - year = {2024}, -} -``` +Thank you for your interest in Timeloopfe! \ No newline at end of file