Performance

Benchmark mode

The application you built earlier includes a benchmark mode that runs the core processing loop multiple times in a hot loop:

• ai-camera-pipelines/bin/cinematic_mode_benchmark
• ai-camera-pipelines/bin/low_light_image_enhancement_benchmark
• ai-camera-pipelines/bin/neural_denoiser_temporal_benchmark_4K

These benchmarks demonstrate the performance improvements enabled by KleidiCV and KleidiAI:

• KleidiCV enhances OpenCV performance with computation kernels optimized for Arm processors.
• KleidiAI accelerates LiteRT+XNNPack inference using AI-optimized micro-kernels tailored for Arm CPUs.

Performance with SME

You can run the benchmarks using the applications you built earlier (with SME2 support).

Run the Background Blur benchmark:

    

        
        
bin/cinematic_mode_benchmark 20 --tflite_file resources/depth_and_saliency_v3_2_assortedv2_w_augment_mobilenetv2_int8_only_ptq.tflite

    

The output is similar to:

    

        
        INFO: Frame rate throttling is turned OFF
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
INFO: Total run time over 20 iterations: 614.724 ms
INFO: Average FPS: 32.5349

        
    

Run the Low Light Enhancement benchmark:

    

        
        
bin/low_light_image_enhancement_benchmark 20 --tflite_file resources/HDRNetLIME_lr_coeffs_v1_1_0_mixed_low_light_perceptual_l1_loss_float32.tflite

    

The output is similar to:

    

        
        INFO: Frame rate throttling is turned OFF
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
INFO: Total run time over 20 iterations: 57.3958 ms
INFO: Average FPS: 348.457

        
    

Last, run the Neural Denoising benchmark:

    

        
        
bin/neural_denoiser_temporal_benchmark_4K 20

    

The output is similar to:

    

        
        INFO: Frame rate throttling is turned OFF
INFO: Total run time over 20 iterations: 36.2114 ms
INFO: Average FPS: 552.312

        
    

From these results, you can see that:

• cinematic_mode_benchmark performed 20 iterations in 614.724 ms, which means 32.5349 FPS
• low_light_image_enhancement_benchmark performed 20 iterations in 57.3958 ms, which means 348.457 FPS
• neural_denoiser_temporal_benchmark_4K performed 20 iterations in 36.2114 ms, which means 552.312 FPS

Performance without SME2

To measure the performances without the benefits of SME2, set ENABLE_SME2=0 and recompile the pipelines as shown in the build page.

You can now re-run the benchmarks and compare the performance benefits of using SME2.

Example performance with a Vivo X300 Android phone

The table table shows the measurements (in FPS, Frames Per Second) measured on a Vivo X300 android phone:

As shown, SME2 brings a dramatic performance improvement. This new power also enables to use much more complex processing algorithms like the spatio-temporal denoising.