Profiling Data Generation
In the simulation framework, the AI Engine simulator can generate a profiling report for the complete application. This
report is generated using the flag
aiesimulator –pkg-dir=Work –profile
Text files and xml files are generated in the directory
aiesimulator_output. Two types of files are generated
for the tile located in column C and row R. The *
_funct reports the number of calls and number of cycles for each
function. The *
_instr is a report that goes down
to the assembly code. To visualize the report, use the Vitis Analyzer.
- Reports the total cycle count, total instruction count, and program size in memory.
- Function Reports
- Shows several key indicators, function by function, in a table and
- Number of calls
- Total function time (cycles and %)
- Total function + descendant time (cycles and %)
- Min/Avg/Max function time (cycles)
- Min/Avg/Max function + descendant time (cycles)
- Program counter Low/High
- Profile Details
- Shows the assembly code, function by function, with useful precisions. The columns are as shown in the following table.
|Instruction||Up to 16 bytes for each line|
|Assembly||Assembly code mnemonic with the full 7-way instruction word|
|Exe-count||Number of times this line has been executed by the processor|
|Cycles||Number of cycles required|
|Wait States||For some instructions you may have memory conflicts which end up into a number of wait-states|
|Relative cycle use within function||Shown as ‘*’ lines where the relative length visually shows the relative cycles use of this instruction within the function|
|Relative cycle use within simulation||Shown as ‘*’ lines where the relative length visually shows the
relative cycles use of this instruction within the simulation
|Relative wait-state use within function||Shown as ‘W’ lines where the relative length visually shows the relative cycles used by wait-states during this instruction within the function|
|Relative wait state use within simulation||Shown as ‘*’ lines where the relative length visually shows the
relative cycles used by wait states during this instruction within
the simulation (including
Performance Debug with Profiling Data
Performance improvement of a design should start by optimizing the function that takes most of the cycles. After it is done, you can optimize functions that take less and less proportion of the cycles. For this purpose, the total function time graph will help you in selecting these functions.
For the optimization itself, you can use pragmas (chess_prepare_for_pipelining, chess_loop_range) for the usual pipelining, unrolling of loops. The Profile Details tab provides insight about wait states. Even if your inner-loop is perfectly optimized, you may lose some cycles due to wait-states. These wait-states occur when there are conflicts in resource access.
- Two reads or one read and one write within the same memory bank, either from the local AI Engine, or two contiguous AI Engines.
- The local AI Engine tries to access a bank (either read or write) while a memory DMA is accessing it for some data transfer.
Here is an example of profile details.