Please refer to the simple example to have an overview of how to define a graph and it nodes and how to generate the C++ code for the static scheduler. This document is only explaining additional details
This example is implementing a dynamic / asynchronous mode.
It is enabled in graph.py with:
conf.asynchronous = True
There is an option to increase the FIFO size compared to their synchronous values. To double the value (increase by 100%) we write:
conf.FIFOIncrease = 100
The graph implemented in this example is:
There is a global iteration count corresponding to one execution of the schedule.
The odd source is generating a value only when the count is odd.
The even source is generating a value only when the count is even.
The processing is adding its inputs. If no data is available on an input, 0 is used.
In case of FIFO overflow or underflow, any node will skip its execution.
All nodes are generating or consuming one sample but the FIFOs have a size of 2 because of the 100% increase requested in the configuration settings.
Thus in this example :
- A sample is not always generated on an edge
- A sample is not always available on an edge
The dataflow on each edge is thus not static and vary between iterations of the schedule
Schedule length = 9
Memory usage 34 bytes
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