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 ...

To address this issue, the researchers proposed the Welder deep learning compiler, which holistically optimizes the memory access efficiency of the end-to-end DNN model. Represented as a data flow ...

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 ...

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 ...

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 ...

All above-mentioned issues have created different data flow mapping method in DNNs accelerator. This paper presents the new data flow mapping on Resnet34, regarding stationaries concepts; moreover, ...