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 KleidiCV and KleidiAI

By default, the OpenCV library is built with KleidiCV support, and LiteRT+XNNPack is built with KleidiAI support.

You can run the benchmarks using the applications you built earlier.

Run the Background Blur benchmark:

    

        
        
bin/cinematic_mode_benchmark 20 resources/depth_and_saliency_v3_2_assortedv2_w_augment_mobilenetv2_int8_only_ptq.tflite

    

The output is similar to:

    

        
        INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
Total run time over 20 iterations: 2028.745 ms

        
    

Run the Low Light Enhancement benchmark:

    

        
        
bin/low_light_image_enhancement_benchmark 20 resources/HDRNetLIME_lr_coeffs_v1_1_0_mixed_low_light_perceptual_l1_loss_float32.tflite

    

The output is similar to:

    

        
        INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
Total run time over 20 iterations: 58.2126 ms

        
    

Last, run the Neural Denoising benchmark:

    

        
        
bin/neural_denoiser_temporal_benchmark_4K 20

    

The output is similar to:

    

        
        Total run time over 20 iterations: 37.6839 ms

        
    

From these results, you can see that:

• cinematic_mode_benchmark performed 20 iterations in 2028.745 ms
• low_light_image_enhancement_benchmark performed 20 iterations in 58.2126 ms
• neural_denoiser_temporal_benchmark_4K performed 20 iterations in 37.6839 ms

Benchmark results without KleidiCV and KleidiAI

To measure the performance without these optimizations, recompile the pipelines using the following flags in your CMake command:

    

        
        
-DENABLE_KLEIDICV:BOOL=OFF -DXNNPACK_ENABLE_KLEIDIAI:BOOL=OFF

    

Re-run the Background Blur benchmark:

    

        
        
bin/cinematic_mode_benchmark 20 resources/depth_and_saliency_v3_2_assortedv2_w_augment_mobilenetv2_int8_only_ptq.tflite

    

The new output is similar to:

    

        
        INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
Total run time over 20 iterations: 2030.5525 ms

        
    

Re-run the Low Light Enhancement benchmark:

    

        
        
bin/low_light_image_enhancement_benchmark 20 resources/HDRNetLIME_lr_coeffs_v1_1_0_mixed_low_light_perceptual_l1_loss_float32.tflite

    

The new output is similar to:

    

        
        INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
Total run time over 20 iterations: 58.0613 ms

        
    

Re-run the Neural Denoising benchmark:

    

        
        
bin/neural_denoiser_temporal_benchmark_4K 20

    

The new output is similar to:

    

        
        Total run time over 20 iterations: 38.0813 ms

        
    

Comparison table and future performance uplift with SME2

As shown, the Background Blur (cinematic_mode_benchmark) and Neural Denoising pipelines gain only a minor improvement, while the low-light enhancement pipeline sees a minor performance degradation (0.26%) when KleidiCV and KleidiAI are enabled.

A major benefit of using KleidiCV and KleidiAI though is that they can automatically leverage new Arm architecture features - such as SME2 (Scalable Matrix Extension v2) - without requiring changes to your application code.

As KleidiCV and KleidiAI operate as performance abstraction layers, any future hardware instruction support can be utilized by simply rebuilding the application. This enables better performance on newer processors without additional engineering effort.