# Linting Profile

Brief

Linting mode is a performance profiling configuration for ops running on the HTP backend.
Detailed profiling report provides per op profiling result by cycle counts instead of time
in microsecs. There is no direct conversion method from cycle count to microsecs because of
the parallelized execution of Ops. Hence it is recommended to use the per op cycle timings
as a reference to compare/measure the relative performance to know which of them are using
lower/higher cycles to finish the execution. Assuming the HTP backend prerequisite is met,
Linting mode is activated by including `--profiling_level=linting` while running snpe-net-run
or by using the `Snpe_SNPEBuilder_SetProfilingLevel` API header to set the profiling level to
`SNPE_PROFILING_LEVEL_LINTING`.

Linting Profile Metrics

On the main thread, each op has to wait for some cycles since the execution of the
last op before the start of its own execution. This wait period can be attributed
to various factors such as scheduling or waiting for some background HVX or DMA
activity to finish. Linting profiling provides the following diagnostic entries per
HTP op:

- **Wait:** The “Wait” entry is a foreground execution descriptor that denotes the
number of cycles spent actually executing the op on the main thread since the previous
op that ran on the main thread.
- **Overlap:** The “Overlap” entry is a background execution descriptor that denotes the
number of cycles spent on at least one background op while this op is executing on the
main thread.
- **Overlap (wait):** The “Overlap (wait)” entry is a background execution descriptor. It
is similar to the “Wait” entry with the exception that the cycles reported in this entry
correspond to the “Wait” period (i.e. cycles spent on at least one background op while
the main thread was waiting).
- **Resources** The “Resources” entry lists the different resources used by the given op.
Namely some combination of HVX, HMX, and DMA.

Background ops that are being waited on by main thread ops are not considered as background
activity and as such do not contribute to the counts reported by the overlap entries. Each
of the overlap entries also has up to 10 indented lines following it indicating the names
of the ops that contributed to the respective overlap cycle count. Please refer to the
[model optimization example](https://docs.qualcomm.com/doc/80-63442-2/topic/linting_profile.html#model-optimization-example) below to see samples of how
snpe-diagview displays the aforementioned Linting profile metrics.

Chrometrace

Like its sibling profiling levels, Linting profile metrics are averaged across all inputs used
during inference and can be viewed using the snpe-diagview tool. However, one advantage of
Linting profile is the ability to export chrometrace JSON files, which can be used to visualize
the op foreground and background execution and overlaps detailed by the Linting profile metrics.

Model Optimization Example

In this section, we walk through an example of how we can use Linting mode and chrometraces to
address a bottleneck in a simple network. [Showcase Model 1](https://docs.qualcomm.com/doc/80-63442-2/topic/linting_profile.html#linting-profiling-showcase-model-original1-figure)
diagram illustrates a model with two branches each performing a couple of convolutions before
their results are used in a sub operation.

**Showcase Model 1**

![Linting Profiling Showcase Model 1](data:image/png;base64,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)

The linting profiling output by snpe-diagview for this model is given below:

...
    
    Per-Graph Execution Times:
    ---------------
    HTP Subnet 0: 4327266 cycles
    
    Layer Times:
    ---------------
      0: Input OpId_2 (cycles) : 0 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 0 cycles
        Overlap (wait) time: 0 cycles
        Resources:
      1: OpId_0 (cycles) : 8036 cycles : DSP
        Wait (Scheduler) time: 629 cycles
        Overlap time: 4770 cycles
        Overlap (wait) time: 565 cycles
        Resources:
      2: model_convStart_Conv2D:OpId_21 (cycles) : 147075 cycles : DSP
        Wait (Scheduler) time: 32 cycles
        Overlap time: 85292 cycles
          model_sub_sub:OpId_57
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 32 cycles
          model_convStart_Conv2D:OpId_21
        Resources: HVX, HMX, DMA
      3: model_tf_op_layer_stride_stride:OpId_24 (cycles) : 146494 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 70807 cycles
          model_add_add:OpId_58
          Output OpId_3
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 0 cycles
        Resources: HVX
      4: model_convLeft1_Conv2D:OpId_34 (cycles) : 288249 cycles : DSP
        Wait (Scheduler) time: 425 cycles
        Overlap time: 195988 cycles
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 304 cycles
          Output OpId_3
          model_add_add:OpId_58
          model_convStart_Conv2D:OpId_21
        Resources: HMX, DMA
      5: model_convRight1_Conv2D:OpId_41 (cycles) : 220391 cycles : DSP
        Wait (Scheduler) time: 803 cycles
        Overlap time: 135268 cycles
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 557 cycles
          Output OpId_3
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Resources: HMX, DMA
      6: model_convRight2_Conv2D:OpId_48 (cycles) : 181016 cycles : DSP
        Wait (Scheduler) time: 1090 cycles
        Overlap time: 69323 cycles
          model_sub_sub:OpId_57
          model_convStart_Conv2D:OpId_21
          Output OpId_3
          model_add_add:OpId_58
        Overlap (wait) time: 489 cycles
          model_sub_sub:OpId_57
          model_convStart_Conv2D:OpId_21
          Output OpId_3
          model_add_add:OpId_58
        Resources: HMX, DMA
      7: model_convLeft2_Conv2D:OpId_55 (cycles) : 233736 cycles : DSP
        Wait (Scheduler) time: 1059 cycles
        Overlap time: 93020 cycles
          model_sub_sub:OpId_57
          model_convStart_Conv2D:OpId_21
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 464 cycles
          model_sub_sub:OpId_57
          model_convStart_Conv2D:OpId_21
          Output OpId_3
          model_add_add:OpId_58
        Resources: HMX, DMA
      8: model_sub_sub:OpId_57 (cycles) : 2165162 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 465046 cycles
          model_sub_sub:OpId_57
          Output OpId_3
          model_add_add:OpId_58
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 0 cycles
        Resources: HVX
      9: model_add_add:OpId_58 (cycles) : 525971 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 481468 cycles
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
          Output OpId_3
          model_add_add:OpId_58
        Overlap (wait) time: 0 cycles
        Resources: HVX
      10: Output OpId_3 (cycles) : 407091 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 115120 cycles
        Overlap (wait) time: 0 cycles
        Resources: HVX
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The linting profiling chrometrace output for this model is given below:

**Showcase Model 1 Chrometrace**

![Linting Profiling Showcase Model 1 Chrometrace](data:image/png;base64,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)

From the output, it is evident that the sub op (OpId\_57) is the most significant contributor
to the total execution time - around 50%. This op also does not have significant parallel op
execution - its Overlap time is 465046 cycles which is about 21.5% of its total execution time -
indicating that this op is a good bottleneck to optimize. We can design an equvalent model as
shown in the [Showcase Model 1 Optimized](https://docs.qualcomm.com/doc/80-63442-2/topic/linting_profile.html#linting-profiling-showcase-model-optimized1-figure)
diagram merging the two branches and replacing the sub op with a convolution with weights manually
designed such that it performs the same task as a sub op.

**Showcase Model 1 Optimized**

![Linting Profiling Showcase Model 1 Optimized](data:image/png;base64,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)

The linting profiling output for this optimized model is given below:

...
    
    Per-Graph Execution Times:
    ---------------
    HTP Subnet 0: 1374349 cycles
    
    Layer Times:
    ---------------
      0: Input OpId_2 (cycles) : 0 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 0 cycles
        Overlap (wait) time: 0 cycles
        Resources:
      1: OpId_0 (cycles) : 3500 cycles : DSP
        Wait (Scheduler) time: 1284 cycles
        Overlap time: 3221 cycles
        Overlap (wait) time: 1268 cycles
        Resources:
      2: model_convStart_Conv2D:OpId_21 (cycles) : 487448 cycles : DSP
        Wait (Scheduler) time: 32 cycles
        Overlap time: 475888 cycles
          Output OpId_3
          model_add_add:OpId_50
          model_tf_op_layer_stride_1_stride_1:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 32 cycles
          model_convStart_Conv2D:OpId_21
        Resources: HVX, HMX, DMA
      3: model_tf_op_layer_stride_1_stride_1:OpId_24 (cycles) : 10422 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 10075 cycles
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_1_stride_1:OpId_24
        Overlap (wait) time: 0 cycles
        Resources: HVX
      4: model_convCombined1_Conv2D:OpId_34 (cycles) : 337711 cycles : DSP
        Wait (Scheduler) time: 82 cycles
        Overlap time: 307394 cycles
          Output OpId_3
          model_tf_op_layer_stride_1_stride_1:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 50 cycles
          Output OpId_3
          model_convStart_Conv2D:OpId_21
        Resources: HMX, DMA
      5: model_convCombined2_Conv2D:OpId_41 (cycles) : 295022 cycles : DSP
        Wait (Scheduler) time: 1184 cycles
        Overlap time: 286062 cycles
          model_add_add:OpId_50
          Output OpId_3
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_1_stride_1:OpId_24
        Overlap (wait) time: 1140 cycles
          model_add_add:OpId_50
          Output OpId_3
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_1_stride_1:OpId_24
        Resources: HMX, DMA
      6: model_subConv_Conv2D:OpId_48 (cycles) : 48720 cycles : DSP
        Wait (Scheduler) time: 1186 cycles
        Overlap time: 46686 cycles
          model_add_add:OpId_50
          model_tf_op_layer_stride_1_stride_1:OpId_24
          Output OpId_3
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 1142 cycles
          model_add_add:OpId_50
          Output OpId_3
          model_convStart_Conv2D:OpId_21
        Resources: HMX, DMA
      7: model_add_add:OpId_50 (cycles) : 110698 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 108524 cycles
          model_add_add:OpId_50
          Output OpId_3
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_1_stride_1:OpId_24
        Overlap (wait) time: 0 cycles
        Resources: HVX
      8: Output OpId_3 (cycles) : 77054 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 75438 cycles
        Overlap (wait) time: 0 cycles
        Resources: HVX
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The total execution time decreases significantly as a result of removing the sub op. All the ops
now have a significant amount of parallel op execution, as evidenced by their respective Overlap
time numbers, indicating good optimization.
[Showcase Model 2](https://docs.qualcomm.com/doc/80-63442-2/topic/linting_profile.html#linting-profiling-showcase-model-original2-figure) diagram illustrates a model
that is similar to the one in the [Showcase Model 1](https://docs.qualcomm.com/doc/80-63442-2/topic/linting_profile.html#linting-profiling-showcase-model-original1-figure)
diagram. The difference is that there is a div op in place of the problematic sub op.

**Showcase Model 2**

![Linting Profiling Showcase Model 2](data:image/png;base64,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)

The linting profiling output for this model is given below:

...
    
    Per-Graph Execution Times:
    ---------------
    HTP Subnet 0: 7866535 cycles
    
    Layer Times:
    ---------------
      0: Input OpId_2 (cycles) : 0 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 0 cycles
        Overlap (wait) time: 0 cycles
        Resources:
      1: OpId_0 (cycles) : 8657 cycles : DSP
        Wait (Scheduler) time: 782 cycles
        Overlap time: 5155 cycles
        Overlap (wait) time: 717 cycles
        Resources:
      2: model_convStart_Conv2D:OpId_21 (cycles) : 148293 cycles : DSP
        Wait (Scheduler) time: 34 cycles
        Overlap time: 86500 cycles
          model_tf_op_layer_RealDiv_RealDiv:OpId_57
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 34 cycles
          model_convStart_Conv2D:OpId_21
        Resources: HVX, HMX, DMA
      3: model_tf_op_layer_stride_stride:OpId_24 (cycles) : 145084 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 70877 cycles
          model_convStart_Conv2D:OpId_21
          model_add_add:OpId_58
          Output OpId_3
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 0 cycles
        Resources: HVX
      4: model_convLeft1_Conv2D:OpId_34 (cycles) : 285476 cycles : DSP
        Wait (Scheduler) time: 431 cycles
        Overlap time: 196212 cycles
          Output OpId_3
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 318 cycles
          Output OpId_3
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Resources: HMX, DMA
      5: model_convRight1_Conv2D:OpId_41 (cycles) : 219298 cycles : DSP
        Wait (Scheduler) time: 804 cycles
        Overlap time: 134711 cycles
          Output OpId_3
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 558 cycles
          Output OpId_3
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Resources: HMX, DMA
      6: model_convRight2_Conv2D:OpId_48 (cycles) : 181198 cycles : DSP
        Wait (Scheduler) time: 1083 cycles
        Overlap time: 68306 cycles
          model_tf_op_layer_RealDiv_RealDiv:OpId_57
          Output OpId_3
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 476 cycles
          model_tf_op_layer_RealDiv_RealDiv:OpId_57
          Output OpId_3
        Resources: HMX, DMA
      7: model_convLeft2_Conv2D:OpId_55 (cycles) : 233731 cycles : DSP
        Wait (Scheduler) time: 1055 cycles
        Overlap time: 91960 cycles
          model_tf_op_layer_RealDiv_RealDiv:OpId_57
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 447 cycles
          model_tf_op_layer_RealDiv_RealDiv:OpId_57
          Output OpId_3
        Resources: HMX, DMA
      8: model_tf_op_layer_RealDiv_RealDiv:OpId_57 (cycles) : 5344081 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 528123 cycles
          model_tf_op_layer_RealDiv_RealDiv:OpId_57
          Output OpId_3
          model_add_add:OpId_58
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 0 cycles
        Resources: HVX
      9: model_add_add:OpId_58 (cycles) : 525199 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 481084 cycles
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_stride:OpId_24
          Output OpId_3
          model_add_add:OpId_58
        Overlap (wait) time: 0 cycles
        Resources: HVX
      10: Output OpId_3 (cycles) : 771320 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 115729 cycles
        Overlap (wait) time: 0 cycles
        Resources: HVX
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Again, the bottleneck for this graph can be identified by examining the main and background utilization
of each op. In this case, the div op is the major contributor to the overall graph execution time with it
taking up 5344081 cycles - about 68% of the total execution time. Only about 10% of this op’s execution
has some parallel background activity which again indicates a good potential for performance gain through
optimization. Replacing the div op with a mul op is a suggested optimization strategy found in the best
practices guidelines. The linting profiler output for the graph optimized with a mult op instead of a div
op is given below:

...
    
    Per-Graph Execution Times:
    ---------------
    HTP Subnet 0: 2741387 cycles
    
    Layer Times:
    ---------------
      0: Input OpId_2 (cycles) : 0 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 0 cycles
        Overlap (wait) time: 0 cycles
        Resources:
      1: OpId_0 (cycles) : 8067 cycles : DSP
        Wait (Scheduler) time: 735 cycles
        Overlap time: 4781 cycles
        Overlap (wait) time: 669 cycles
        Resources:
      2: model_convStart_Conv2D:OpId_21 (cycles) : 147478 cycles : DSP
        Wait (Scheduler) time: 32 cycles
        Overlap time: 86319 cycles
          model_multiply_mul:OpId_57
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 32 cycles
          model_convStart_Conv2D:OpId_21
        Resources: HVX, HMX, DMA
      3: model_tf_op_layer_stride_stride:OpId_24 (cycles) : 145396 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 70208 cycles
          model_convStart_Conv2D:OpId_21
          model_add_add:OpId_58
          Output OpId_3
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 0 cycles
        Resources: HVX
      4: model_convLeft1_Conv2D:OpId_34 (cycles) : 287130 cycles : DSP
        Wait (Scheduler) time: 430 cycles
        Overlap time: 198222 cycles
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 308 cycles
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Resources: HMX, DMA
      5: model_convRight1_Conv2D:OpId_41 (cycles) : 219409 cycles : DSP
        Wait (Scheduler) time: 806 cycles
        Overlap time: 135286 cycles
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Overlap (wait) time: 558 cycles
          Output OpId_3
          model_tf_op_layer_stride_stride:OpId_24
          model_convStart_Conv2D:OpId_21
        Resources: HMX, DMA
      6: model_convRight2_Conv2D:OpId_48 (cycles) : 181465 cycles : DSP
        Wait (Scheduler) time: 1068 cycles
        Overlap time: 69160 cycles
          model_multiply_mul:OpId_57
          model_convStart_Conv2D:OpId_21
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 467 cycles
          model_multiply_mul:OpId_57
          model_convStart_Conv2D:OpId_21
          Output OpId_3
          model_add_add:OpId_58
        Resources: HMX, DMA
      7: model_convLeft2_Conv2D:OpId_55 (cycles) : 233619 cycles : DSP
        Wait (Scheduler) time: 1055 cycles
        Overlap time: 92740 cycles
          model_multiply_mul:OpId_57
          model_convStart_Conv2D:OpId_21
          Output OpId_3
          model_add_add:OpId_58
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 445 cycles
          model_multiply_mul:OpId_57
          model_convStart_Conv2D:OpId_21
          Output OpId_3
          model_add_add:OpId_58
        Resources: HMX, DMA
      8: model_multiply_mul:OpId_57 (cycles) : 737978 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 437784 cycles
          model_multiply_mul:OpId_57
          Output OpId_3
          model_add_add:OpId_58
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_stride:OpId_24
        Overlap (wait) time: 0 cycles
        Resources: HVX
      9: model_add_add:OpId_58 (cycles) : 527450 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 481714 cycles
          model_convStart_Conv2D:OpId_21
          model_tf_op_layer_stride_stride:OpId_24
          Output OpId_3
          model_add_add:OpId_58
        Overlap (wait) time: 0 cycles
        Resources: HVX
      10: Output OpId_3 (cycles) : 249264 cycles : DSP
        Wait (Scheduler) time: 0 cycles
        Overlap time: 117890 cycles
        Overlap (wait) time: 0 cycles
        Resources: HVX
    Copy to clipboard

There is a noticeable reduction in the total graph execute time and the ops also have better background
utilization indicating better optimization than before.
Next, [Showcase Model 3](https://docs.qualcomm.com/doc/80-63442-2/topic/linting_profile.html#linting-profiling-showcase-model-original3-figure) diagram illustrates a model that is
similar to the one in [Showcase Model 1 Optimized](https://docs.qualcomm.com/doc/80-63442-2/topic/linting_profile.html#linting-profiling-showcase-model-optimized1-figure)
diagram. The difference is that the ReLU ops have been replaced with PReLU ops.

**Showcase Model 3**

![Linting Profiling Showcase Model 3](data:image/png;base64,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)

The linting profiler output for this model is given below:

...
    
    Per-Graph Execution Times:
    ---------------
    HTP Subnet 0: 2789467 cycles
    
    Layer Times:
    ---------------
    0: Input OpId_2 (cycles) : 0 cycles : DSP
      Wait (Scheduler) time: 0 cycles
      Overlap time: 0 cycles
      Overlap (wait) time: 0 cycles
      Resources:
    1: OpId_0 (cycles) : 3411 cycles : DSP
      Wait (Scheduler) time: 1226 cycles
      Overlap time: 3173 cycles
      Overlap (wait) time: 1194 cycles
      Resources:
    2: model_convStart_Conv2D:OpId_21 (cycles) : 589431 cycles : DSP
      Wait (Scheduler) time: 957 cycles
      Overlap time: 41199 cycles
        Output OpId_3
        model_add_add:OpId_54
        model_preluCombined1_add:OpId_37
        model_convStart_Conv2D:OpId_21
      Overlap (wait) time: 72 cycles
        Output OpId_3
        model_convStart_Conv2D:OpId_21
      Resources: HVX, HMX, DMA
    3: model_tf_op_layer_stride_1_stride_1:OpId_24 (cycles) : 0 cycles : DSP
      Wait (Scheduler) time: 0 cycles
      Overlap time: 0 cycles
      Overlap (wait) time: 0 cycles
      Resources:
    4: model_convCombined1_Conv2D:OpId_34 (cycles) : 165119 cycles : DSP
      Wait (Scheduler) time: 1089 cycles
      Overlap time: 155164 cycles
        model_preluCombined1_add:OpId_37
        Output OpId_3
        model_add_add:OpId_54
        model_convStart_Conv2D:OpId_21
      Overlap (wait) time: 977 cycles
        model_preluCombined1_add:OpId_37
        Output OpId_3
        model_add_add:OpId_54
        model_convStart_Conv2D:OpId_21
      Resources: HMX, DMA
    5: model_preluCombined1_add:OpId_37 (cycles) : 27315 cycles : DSP
      Wait (Scheduler) time: 0 cycles
      Overlap time: 9431 cycles
        model_convStart_Conv2D:OpId_21
      Overlap (wait) time: 0 cycles
      Resources: HVX
    6: model_convCombined2_Conv2D:OpId_43 (cycles) : 805490 cycles : DSP
      Wait (Scheduler) time: 81 cycles
      Overlap time: 251743 cycles
        model_add_add:OpId_54
        Output OpId_3
        model_preluCombined1_add:OpId_37
        model_preluCombined2_add:OpId_46
        model_convStart_Conv2D:OpId_21
      Overlap (wait) time: 62 cycles
        Output OpId_3
        model_convStart_Conv2D:OpId_21
      Resources: HMX, DMA
    7: model_preluCombined2_add:OpId_46 (cycles) : 0 cycles : DSP
      Wait (Scheduler) time: 0 cycles
      Overlap time: 0 cycles
      Overlap (wait) time: 0 cycles
      Resources: HVX
    8: model_subConv_Conv2D:OpId_52 (cycles) : 666721 cycles : DSP
      Wait (Scheduler) time: 34 cycles
      Overlap time: 180805 cycles
        model_add_add:OpId_54
        Output OpId_3
        model_convStart_Conv2D:OpId_21
        model_preluCombined2_add:OpId_46
      Overlap (wait) time: 13 cycles
        model_convStart_Conv2D:OpId_21
      Resources: HMX, DMA
    9: model_add_add:OpId_54 (cycles) : 62806 cycles : DSP
      Wait (Scheduler) time: 0 cycles
      Overlap time: 57481 cycles
        model_add_add:OpId_54
        Output OpId_3
        model_preluCombined1_add:OpId_37
        model_preluCombined2_add:OpId_46
        model_convStart_Conv2D:OpId_21
      Overlap (wait) time: 0 cycles
      Resources: HVX
    10: Output OpId_3 (cycles) : 465781 cycles : DSP
      Wait (Scheduler) time: 0 cycles
      Overlap time: 430560 cycles
      Overlap (wait) time: 0 cycles
      Resources: HVX
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The usual sign indicating bottlenecks is present here as well. There are multiple ops with low parallel
execution. PReLU ops are some of the background ops that executed for these ops and the best practices
guidelines suggest that PReLU ops should be replaced with ReLU ops. Changing the graph by replacing the
PReLU ops with ReLU gives us the same model as the one shown in the
[Showcase Model 1 Optimized](https://docs.qualcomm.com/doc/80-63442-2/topic/linting_profile.html#linting-profiling-showcase-model-optimized1-figure) diagram which is
much better optimized as explained before.

Caveats

Since Linting profile is only available for HTP, non-HTP subnets will silently fall back to
the next most descriptive profiling level, Detailed, while HTP subnets will be executed with
Linting mode enabled as requested by the user. Additionally, for multi-subnet networks with a
combination of HTP and non-HTP subnets, snpe-diagview will generate separate chrometraces only
for each HTP subnet. For example, when running inference (with Linting profiling enabled) on
a network with 3 HTP subnets and 2 non-HTP subnets, snpe-diagview is expected to produce 3
chrometraces when invoked with `--chrometrace`.

Last Published: Oct 02, 2025

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