Mapping data flows are authored using a design surface know as the data flow graph. In the graph, transformation logic is built left-to-right and additional data streams are added top-down. To add a ...

The majority of DNN accelerator use parallelism Processing Elements (PEs) in order to lessen hardware costs. Regarding high volume of input data in image processing, lessening computing runtime is ...

This project is about designing and generating synthesizable high level state machine description from the Data flow graph in Verilog while providing scheduling alternatives like LIST_L and LIST_R ...

Astra is a compilation and execution framework that optimizes execution of a deep learning training job. Instead of treating the computation as a generic data flow graph, Astra exploits domain ...

Performing Deep Neural Network (DNN) computation on hardware accelerators efficiently is challenging. Existing DNN frameworks and compilers often treat the DNN operators in a data flow graph (DFG) as ...

Google today released TensorFlow Graph Neural Networks (TF-GNN) in alpha, a library designed to make it easier to work with graph structured data using TensorFlow, its machine learning framework.