Stream - Multi-core accelerator design space exploration with layer-fused scheduling
Project description
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.
More information with respect to the capabilities of Stream can be found in the following paper: Towards Heterogeneous Multi-core Accelerators Exploiting Fine-grained Scheduling of Layer-Fused Deep Neural Networks.
Documentation
Documentation for Stream is underway!
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for stream_dse-0.0.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 039fb651acd1e7b5cd33b70ec2aea4b714c3f847d8e1756209eaa7bdd7b7296e |
|
MD5 | e44a8f33030ab656dbe5295da9567b35 |
|
BLAKE2b-256 | 7b9afb181344ab5afa69c84ab4162e3fd89c6c6ed9d3f4f562c9dd25ac4cb87a |