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| # 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! |