Basic Iterative Graph Execution - 2024.1 English

AI Engine-ML Kernel and Graph Programming Guide (UG1603)

Document ID
UG1603
Release Date
2024-06-06
Version
2024.1 English

The following graph control API shows how to use graph APIs to initialize, run, wait, and terminate graphs for a specific number of iterations. A graph object mygraph is declared using a pre-defined graph class called simpleGraph. Then, in the main application, this graph object is initialized and run. The init() method loads the graph to the AI Engine-ML array at prespecified AI Engine-ML tiles. This includes loading the ELF binaries for each AI Engine-ML, configuring the stream switches for routing, and configuring the DMAs for I/O. It leaves the processors in a disabled state. The run() method starts the graph execution by enabling the processors. The run API is where a specific number of iterations of the graph can be run by supplying a positive integer argument at run time. This form is useful for debugging your graph execution.

#include "project.h" 
simpleGraph mygraph; 

int main(void) { 
  mygraph.init(); 
  mygraph.run(3); // run 3 iterations 
  mygraph.wait(); // wait for 3 iterations to finish 
  mygraph.run(10); // run 10 iterations 
  mygraph.end(); // wait for 10 iterations to finish 
  return 0; 
}

The API wait() is used to wait for the first run to finish before starting the second run. wait has the same blocking effect as end except that it allows re-running the graph again without having to re-initialize it. Calling run back-to-back without an intervening wait to finish that run can have an unpredictable effect because the run API modifies the loop bounds of the active processors of the graph.

Graph Iteration

A graph can have multiple kernels, input and output ports. The graph connectivity, which is equivalent to the nets in a data flow graph is either between the kernels, between kernel and input ports, or between kernel and output ports, and can be configured as a connection. A graph runs for an iteration when it consumes data samples equal to the buffer or stream of data expected by the kernels in the graph, and produces data samples equal to the buffer or stream of data expected at the output of all the kernels in the graph.