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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.

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, “Stream: Design Space Exploration of Layer-Fused DNNs on Heterogeneous Dataflow Accelerators”.

Install required packages:

pip install -r requirements.txt

The first run

git checkout tutorial
python lab1/main.py

Documentation

You can find extensive documentation of Stream here.

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Multi-core HW accelerator mapping optimization framework for layer-fused ML workloads.

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