Metadata-Version: 2.1
Name: stream-dse
Version: 0.0.8
Summary: Stream - Multi-core accelerator design space exploration with layer-fused scheduling
Author-email: Arne Symons <arne.symons@kuleuven.be>, Linyan Mei <linyan.mei@kuleuven.be>
License: BSD 3-Clause License
        
        Copyright (c) 2023, MICAS (KU Leuven)
        
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Project-URL: Homepage, https://github.com/ZigZag-Project/stream
Keywords: stream,multi-core,accelerator,layer-fused,scheduling,zigzag,dse,design-space-exploration,machine-learning,deep-learning,mapping
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

# 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!
