samsung SM-S938B
Geekbench ML Score
1986
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | October 22 2025 02:14 PM |
| Views | 7 |
System Information
| System Information | |
|---|---|
| Operating System | Android 16 |
| Model | samsung SM-S938B |
| Model ID | samsung SM-S938B |
| Motherboard | sun |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | Qualcomm ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 81 architecture 8 variant 3 part 1 revision 4 |
| Base Frequency | 3.53 GHz |
| Cluster 1 | 6 Cores @ 3.53 GHz |
| Cluster 2 | 2 Cores @ 4.47 GHz |
| Memory Information | |
|---|---|
| Size | 10.85 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
1188
222.4 IPS |
|
|
Image Classification (F16)
|
100% |
1098
205.4 IPS |
|
|
Image Classification (I8)
|
97% |
1491
279.0 IPS |
|
|
Image Segmentation (F32)
|
100% |
1547
25.8 IPS |
|
|
Image Segmentation (F16)
|
100% |
1512
25.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
1785
29.8 IPS |
|
|
Pose Estimation (F32)
|
100% |
2117
2.56 IPS |
|
|
Pose Estimation (F16)
|
100% |
2067
2.50 IPS |
|
|
Pose Estimation (I8)
|
100% |
8530
10.3 IPS |
|
|
Object Detection (F32)
|
100% |
1131
84.4 IPS |
|
|
Object Detection (F16)
|
100% |
1130
84.3 IPS |
|
|
Object Detection (I8)
|
61% |
1879
140.3 IPS |
|
|
Face Detection (F32)
|
100% |
2307
27.4 IPS |
|
|
Face Detection (F16)
|
100% |
2178
25.9 IPS |
|
|
Face Detection (I8)
|
86% |
4304
51.2 IPS |
|
|
Depth Estimation (F32)
|
100% |
2187
17.0 IPS |
|
|
Depth Estimation (F16)
|
100% |
2033
15.8 IPS |
|
|
Depth Estimation (I8)
|
95% |
4177
32.4 IPS |
|
|
Style Transfer (F32)
|
100% |
3441
4.53 IPS |
|
|
Style Transfer (F16)
|
100% |
3398
4.47 IPS |
|
|
Style Transfer (I8)
|
98% |
9549
12.6 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
1309
46.8 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1303
46.5 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
4315
154.1 IPS |
|
|
Text Classification (F32)
|
100% |
1105
1.59 KIPS |
|
|
Text Classification (F16)
|
100% |
1095
1.57 KIPS |
|
|
Text Classification (I8)
|
92% |
869
1.25 KIPS |
|
|
Machine Translation (F32)
|
100% |
1885
34.7 IPS |
|
|
Machine Translation (F16)
|
100% |
1839
33.9 IPS |
|
|
Machine Translation (I8)
|
64% |
997
18.4 IPS |
