Welcome to Stream’s documentation!¶
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.
While the ZigZag framework was built to explore mappings of DNN workloads on single-core architecture in a layer-by-layer fashion, Stream extends this idea by two dimensions (see images below). Firstly, it allows the exploration of multi-core architectures as well. Secondly, Stream introduces layer-fused execution of DNN workloads while ZigZag is limited to layer-by-layer execution. Besides this, Stream allows to perform a design space exploration of workloads which consists of many layers. By employing an genetic algorithm, Stream finds optimal layer-core allocations of these multi-layer workloads on multi-core architectures.
![_images/overview-frameworks.jpg](_images/overview-frameworks.jpg)
This video provides you with an introduction of Stream. You can read in one of our publications about the advantages of the support of layer-fused processing and (heterogeneous) multi-core systems.