https://github.com/artecs-group/posit-hls
Generating Posit-Based Accelerators With High-Level Synthesis
https://github.com/artecs-group/posit-hls
computer-arithmetic floating-point hls posit
Last synced: 24 days ago
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Generating Posit-Based Accelerators With High-Level Synthesis
- Host: GitHub
- URL: https://github.com/artecs-group/posit-hls
- Owner: artecs-group
- License: gpl-3.0
- Created: 2023-09-04T14:10:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-05T11:29:28.000Z (10 months ago)
- Last Synced: 2025-03-30T20:22:23.740Z (about 2 months ago)
- Topics: computer-arithmetic, floating-point, hls, posit
- Language: Jupyter Notebook
- Homepage:
- Size: 9.82 MB
- Stars: 2
- Watchers: 6
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Generating Posit-Based Accelerators With High-Level Synthesis
This repo contains the code to reproduce the experiments, as well as the results, from the paper
[Generating Posit-Based Accelerators With High-Level Synthesis](https://doi.org/10.1109/TCSI.2023.3299009).
It can also serve as an example of how to use HLS for the generation of posit arithmetic accelerators.
### Prerequisites
You need to install a modified version of the [Bambu framework](https://panda.deib.polimi.it/?page_id=31). More precisely, clone the following repo, which contains the necessary modifications to make Bambu work with posit units: https://github.com/RaulMurillo/PandA-bambu. Then follow the [official installation instructions](https://panda.deib.polimi.it/?page_id=88).
⚠️ Note:
Ensure you install Bambu from the `posit_utils` branch.
After the installation, you must pre-characterize the target device you wish to use. To that end, you need to use the Eucalyptus tool. You can use the `characterize_device_custom.sh` script from `PandA-bambu/etc/devices/` directory with the following arguments:
```bash
--devices= --eucalyptus=/bin/eucalyptus --spider=//bin/spider -j8 -c"--flopoco=posit"
```
The resulting `.xml` file must be copied into the corresponding folder within `PandA-bambu/etc/devices/`.
Finally, compile and install the tool again.
⚠️ Note:
This makes Bambu work just with the posit format, breaking compatibility with floating-point. To restore the original functionality, execute the previous script without the `-c"--flopoco=posit"` argument, and repeat the following steps. You can keep the different `.xml` files and just replace and compile (but the name must be preserved).
### Usage
To generate a posit-based accelerator, take any C program using floating-point numbers, and use the option `--flopoco=posit` when calling Bambu. The `float` and `double` types will be automatically translated by 32 or 64-bit posits.
⚠️ Note:
The automatic simulation of Bambu will fail when using posits, since it relies on C floats to simulate the behavior of the accelerator. Generating testbench with all 0's usually avoids this issue.
### Structure of the repo
* Both `Bambu` and `Vitis_HLS` contain a similar structure, with one subdir per test. Operation-level applications can be found at the root, while real applications are grouped under the `polybench` folder.
* The sources from `Bambu` can serve as examples of how to use Bambu to generate posit-based accelerators. For more details, see the `bambu.sh` scripts.
* In `error_eval`, the numerical error of the different PolyBench applications is computed via software emulation (for faster computation).
### Publications
Overview paper:
> R. Murillo, A. A. D. Barrio, G. Botella and C. Pilato, "Generating Posit-Based Accelerators With High-Level Synthesis," in IEEE Transactions on Circuits and Systems I: Regular Papers, doi: [10.1109/TCSI.2023.3299009](https://doi.org/10.1109/TCSI.2023.3299009).
If you find this project useful, please do not forget to cite this paper.
### Acknowledgments
This work was supported in part by MCIN/AEI/10.13039/ 501100011033 under Grant PID2021-123041OB-I00; in part by the “ERDF—A Way of Making Europe;” in part by the 2020 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation, under Grant PR2003_20/01; in part by CM under Grant S2018/TCS-4423; in part by the EU Horizon 2020 Programme under Grant 957269; and in part by the HiPEAC6 Network funded by the EU Horizon 2020 Programme under Grant ICT-2019-871174.