samsung SM-S928B
Geekbench ML Score
1400
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 17 2024 05:34 PM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-S928B |
| Model ID | samsung SM-S928B |
| Motherboard | pineapple |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3458 revision 1 |
| Base Frequency | 2.26 GHz |
| Cluster 1 | 2 Cores @ 2.27 GHz |
| Cluster 2 | 2 Cores @ 2.96 GHz |
| Cluster 3 | 3 Cores @ 3.15 GHz |
| Cluster 4 | 1 Core @ 3.40 GHz |
| Memory Information | |
|---|---|
| Size | 10.81 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
731
136.8 IPS |
|
|
Image Classification (F16)
|
100% |
1153
215.7 IPS |
|
|
Image Classification (I8)
|
99% |
751
140.6 IPS |
|
|
Image Segmentation (F32)
|
100% |
976
16.3 IPS |
|
|
Image Segmentation (F16)
|
100% |
1612
26.9 IPS |
|
|
Image Segmentation (I8)
|
98% |
1482
24.8 IPS |
|
|
Pose Estimation (F32)
|
100% |
1065
1.29 IPS |
|
|
Pose Estimation (F16)
|
100% |
2649
3.21 IPS |
|
|
Pose Estimation (I8)
|
100% |
2638
3.19 IPS |
|
|
Object Detection (F32)
|
100% |
374
27.9 IPS |
|
|
Object Detection (F16)
|
100% |
433
32.4 IPS |
|
|
Object Detection (I8)
|
62% |
272
20.3 IPS |
|
|
Face Detection (F32)
|
100% |
2872
34.2 IPS |
|
|
Face Detection (F16)
|
97% |
3306
39.3 IPS |
|
|
Face Detection (I8)
|
86% |
2923
34.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
1908
14.8 IPS |
|
|
Depth Estimation (F16)
|
100% |
4145
32.1 IPS |
|
|
Depth Estimation (I8)
|
95% |
4068
31.5 IPS |
|
|
Style Transfer (F32)
|
100% |
7191
9.46 IPS |
|
|
Style Transfer (F16)
|
100% |
11808
15.5 IPS |
|
|
Style Transfer (I8)
|
98% |
11747
15.5 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
568
20.3 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1464
52.3 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1415
50.5 IPS |
|
|
Text Classification (F32)
|
100% |
431
619.7 IPS |
|
|
Text Classification (F16)
|
100% |
431
618.8 IPS |
|
|
Text Classification (I8)
|
92% |
714
1.03 KIPS |
|
|
Machine Translation (F32)
|
100% |
687
12.6 IPS |
|
|
Machine Translation (F16)
|
100% |
668
12.3 IPS |
|
|
Machine Translation (I8)
|
64% |
798
14.7 IPS |
