| Name | stream-dse JSON |
| Version |
0.0.8
JSON |
| download |
| home_page | |
| Summary | Stream - Multi-core accelerator design space exploration with layer-fused scheduling |
| upload_time | 2023-09-05 07:38:26 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | BSD 3-Clause License Copyright (c) 2023, MICAS (KU Leuven) Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
stream
multi-core
accelerator
layer-fused
scheduling
zigzag
dse
design-space-exploration
machine-learning
deep-learning
mapping
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# Stream
Stream is a HW architecture-mapping design space exploration (DSE) framework for multi-core deep learning accelerators. The mapping can be explored at different granularities, ranging from classical layer-by-layer processing to fine-grained layer-fused processing. Stream builds on top of the ZigZag DSE framework, found [here](https://zigzag-project.github.io/zigzag/).
More information with respect to the capabilities of Stream can be found in the following paper:
[A. Symons, L. Mei, S. Colleman, P. Houshmand, S. Karl and M. Verhelst, “Towards Heterogeneous Multi-core Accelerators Exploiting Fine-grained Scheduling of Layer-Fused Deep Neural Networks”, <i>arXiv e-prints</i>, 2022. doi:10.48550/arXiv.2212.10612.](https://arxiv.org/abs/2212.10612)
## Install required packages:
```
> pip install -r requirements.txt
```
## The first run
```
> cd stream
> python api.py
```
## Documentation
Documentation for Stream is underway!
Raw data
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