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Performance


The tools of Zebra Aurora™ Vision software are highly optimized for modern multi-core processors with SSE/AVX technology. The table below shows the results of a vision software performance benchmark.

Vision Software Benchmark

Filter Zebra Aurora™ Vision Studio 4.12 Another product OpenCV 4.2
Image negation 0.030 ms 0.032 ms 0.025 ms
Add two images (pixel by pixel) 0.029 ms 0.047 ms 0.036 ms
Image difference (pixel by pixel) 0.036 ms 0.045 ms 0.030 ms
RGB to HSV conversion (3xUINT8) 0.127 ms 1.026 ms 0.129 ms
Gauss filter 3x3 0.031 ms 0.035 ms 0.037 ms
Gauss filter 5x5 0.033 ms 0.073 ms 0.052 ms
Gauss filter 21x21 (std-dev: 4.3) 0.311 ms 0.355 ms 0.240 ms
Mean filter 21x21 0.100 ms 0.102 ms 0.291 ms
Image erosion 3x3 0.030 ms 0.035 ms 0.050 ms
Image erosion 5x5 0.030 ms 0.036 ms 0.059 ms
Sobel gradient magnitude (sum) 0.032 ms 0.035 ms
Sobel gradient magnitude (hypot) 0.034 ms 0.040 ms
Threshold to region 0.043 ms 0.076 ms
Splitting region into blobs 0.119 ms 0.206 ms
Bilinear image resize 0.131 ms 0.108 ms 0.052 ms

The above results correspond to 640x480 resolution, 1xUINT8 on an Intel Core i5 - 3.2 GHz machine. In order to elimate the non-random component of measurement error, the repetition count of each operation was increased by factor of 10, 30 times. It leads to the following repetition sequence: 10, 20, 30, ..., 300. Later for the obtained execution times a straight line was fitted. In this approach constant error related to the start and stop of measurements is reflected by the line shift while the execution time is expressed in line slope. To increase the precision of measurements big images were tested and the results were normalized. Note also that the functions from the different libraries do not always produce exactly the same output data.


SSE & Multi-Core Optimization

Filters of Zebra Aurora™ Vision Studio are optimized for SSE/AVX/NEON technology and for multi-core processors. Speed-up factors that can be achieved with these techniques are however highly dependent on the particular operator. Simple pixel-by-pixel transforms after SSE-based optimizations already reach memory bandwidth limits. On the other hand, more complex filters such as Gauss smoothing can achieve even 10 times lower execution times than with C++ optimizations only.

CPU Benchmark

The table below demonstrates how well different processors perform when executing our software tools (the higher the better). You can use it as a reference when choosing hardware for your application.

Benchmark category Overall result
Device description Executor Engine Image processing Image analysis Region processing Applications
Intel Atom D525
1.80GHz / 1MB cache / 2 cores / 4 GB RAM
54.9 32.7 41.1 61.7 53.1 48.7
Intel Core 2 Duo T6400
2.00GHz / 2MB cache / 2 cores / 3 GB RAM
54.9 79.4 87.1 108.2 105.4 87.0
Intel Atom E3845
1.91GHz / 2MB cache / 4 cores / 4 GB RAM
100.0 100.0 100.0 100.0 100.0 100.0
Intel Pentium N4200
1.10 GHz / 2MB cache / 4 cores/ 4 GB RAM
193.5 204.2 157.3 143.6 167.3 173.2
AMD FX-4100 Quad-Core
3.60 GHz / 8MB cache / 4 cores/ 8 GB RAM
112.3 213.4 164.8 218.7 174.6 176.7
AMD Athlon II X2 270
3.40 GHz / 2MB cache / 2 cores/ 8 GB RAM
311.6 136.8 171.6 210.0 212.0 208.4
Intel Core-i7 3612QM
2.10GHz / 6MB cache / 4 cores/ 4 GB RAM
427.8 534.6 303.6 295.9 352.6 382.9
Intel Core-i7 2600K
3.40GHz / 8MB cache / 4 cores/ 8 GB RAM
507.6 593.4 346.8 345.9 393.1 437.4
Intel Core-i5 3470
3.20GHz / 6MB cache / 4 cores/ 16 GB RAM
545.3 628.1 355.1 324.7 403.6 455.0
Intel Core-i5 3570K
3.40GHz / 6MB cache / 4 cores/ 8 GB RAM
554.6 645.5 359.0 360.4 416.5 467.2
Intel Core-i5 4460
3.20GHz / 6MB cache / 4 cores/ 16 GB RAM
611.6 667.6 366.6 356.9 421.3 484.8
Intel Core-i7 4800MQ
2.70GHz / 6MB cache / 4 cores/ 12 GB RAM
628.3 678.7 380.5 378.9 420.8 483.5
Intel Core-i7 6700HQ
2.60GHz / 6MB cache / 4 cores/ 16 GB RAM
641.8 710.0 365.9 366.8 416.3 500.2
Intel Core-i7 4800MQ
2.70GHz / 6MB cache / 4 cores/ 16 GB RAM
640.2 699.1 380.9 378.8 412.6 502.3
Intel Core-i5 6500
3.20GHz / 6MB cache / 4 cores/ 16 GB RAM
663.7 794.0 395.7 390.2 458.1 540.3
Intel Core-i5 7500
3.40GHz / 6MB cache / 4 cores/ 16 GB RAM
684.3 830.1 422.0 406.8 492.6 567.1
Intel Core-i7 4790K
4.00GHz / 8MB cache / 4 cores/ 16 GB RAM
798.2 887.5 474.7 461.1 550.1 634.3
AMD Ryzen 7 2700X
3.70GHz / 20MB cache / 8 cores/ 16 GB RAM
667.9 1407.1 535.9 439.0 419.6 693.9
Intel Core-i7 8700K
3.70GHz / 12MB cache / 6 cores/ 16 GB RAM
862.5 1364.7 587.8 491.3 594.3 780.1

Higher value means better performance.
The test measures execution time for constant number of operations. Results are normalized.
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Deep Learning Benchmark

The table below demonstrates how well different hardware configurations perform when executing our Deep Learning tools (the higher the better).
You can use it as a reference when choosing hardware for your application.

Hardware configuration Deep Learning Network Overall result
CPU / RAM / GPU / Compute Capability/ NVIDIA Driver
Classify Object (CO) Detect Anomalies 2 (DA2) Detect Anomalies 1 Global (DA1G) Detect Anomalies 1 Local (DA1L) Detect Features (DF) Instance Segmentation (IS) Locate Points (LP)
Intel Core-i5 9400F 2,90GHz / 16 GB RAM
GeForce GT 730 2GB / 3.5 / 452.06
35.7 5.7 24.0 6.3 6.9 15.0 7.0 7.4
AMD Ryzen 7 2700X Eight-Core / 16 GB RAM
118.2 30.1 64.3 12.4 13.6 92.7 18.4 20.2
Intel Core-i5 7500 3,40GHz / 16 GB RAM
122.8 26.9 58.2 14.8 13.3 83.9 15.0 20.5
Intel Core-i7 9750H 2,60GHz / 16 GB RAM (Laptop)
58.9 26.6 59.1 18.8 13.6 88.3 16.3 22.6
Intel Core-i7 8700K 3,70GHz / 16 GB RAM
186.0 32.6 75.6 17.6 14.9 102.9 19.1 24.4
Intel Core-i5 9400F 2,90GHz / 16 GB RAM
164.3 34.9 82.6 22.2 18.9 105.3 21.6 29.1
Intel Core-i9 11900KF 3,50GHz / 32 GB RAM
245.5 43.9 70.5 40.3 43.2 172.7 68.6 53.1
Intel Core-i7 9750H 2,60GHz / 16 GB RAM
GeForce RTX 2060 6GB / 7.5 / 445.87 (Laptop)
68.5 135.1 108.5 94.8 85.4 96.5 69.2 96.5
AMD Ryzen 7 2700X Eight-Core / 16 GB RAM
GeForce GTX 1060 6GB / 6.1 / 452.06
102.2 99.1 92.8 102.8 99.5 97.8 100.0 99.6
Intel Core-i5 7500 3,40GHz / 16 GB RAM
GeForce GTX 1060 6GB / 6.1 / 445.87
100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Intel Core-i7 8700K 3,70GHz / 16 GB RAM
GeForce GTX 1060 6GB / 6.1 / 452.06
101.6 103.9 90.0 101.0 100.5 96.0 105.8 100.5
Intel Core-i7 8700K 3,70GHz / 32 GB RAM
GeForce GTX 1070 8GB / 6.1 / 452.06
82.7 136.0 90.9 129.6 133.6 106.3 134.6 124.1
Intel Core-i5 7500 3,40GHz / 16 GB RAM
GeForce RTX 2060 6GB / 7.5 / 441.87
102.7 157.7 135.4 142.8 148.8 133.1 134.7 143.3
Intel Core-i5 7500 3,40GHz / 16 GB RAM
GeForce GTX 1080 8GB / 6.1 / 452.06
109.0 158.9 108.4 161.5 167.7 127.3 161.4 150.5
Intel Core-i5 9400F 2,90GHz / 16 GB RAM
GeForce RTX 2060 SUPER 8GB / 7.5 / 452.06
99.4 192.5 167.7 173.8 182.4 155.5 168.6 173.7
Intel Core-i5 9400F 2,90GHz / 16 GB RAM
GeForce RTX 3060Ti 8GB / 8.6 / 465.21
99.7 244.3 201.7 249.8 556.0 172.8 511.7 259.9
Intel Core-i9 11900KF 3,50GHz / 32 GB RAM
GeForce RTX 3070 8GB / 8.6 / 457.51
161.6 276.8 134.3 274.2 569.7 175.8 594.9 270.8

Higher value means better performance.
The test measures execution time for selected Deep Learning tools. Results are normalized.
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