samsung SM-S938B
Geekbench AI Score
703
Single Precision Score
703
Half Precision Score
1600
Quantized Score
Geekbench AI 1.2.0 for Android AArch64
Result Information
| User | mihun70 |
| Upload Date | February 25 2025 03:39 PM |
| Views | 6 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Qualcomm ARMv8 |
| System Information | |
|---|---|
| Operating System | Android 15 |
| 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 | 10.86 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
433
80.6 IPS |
|
|
Image Classification (HP)
|
100% |
429
79.8 IPS |
|
|
Image Classification (Q)
|
99% |
1091
203.6 IPS |
|
|
Image Segmentation (SP)
|
100% |
550
8.92 IPS |
|
|
Image Segmentation (HP)
|
100% |
551
8.94 IPS |
|
|
Image Segmentation (Q)
|
98% |
1100
17.9 IPS |
|
|
Pose Estimation (SP)
|
100% |
729
0.85 IPS |
|
|
Pose Estimation (HP)
|
100% |
713
0.83 IPS |
|
|
Pose Estimation (Q)
|
98% |
2860
3.35 IPS |
|
|
Object Detection (SP)
|
100% |
400
31.7 IPS |
|
|
Object Detection (HP)
|
100% |
402
31.9 IPS |
|
|
Object Detection (Q)
|
87% |
1093
87.6 IPS |
|
|
Face Detection (SP)
|
100% |
987
11.7 IPS |
|
|
Face Detection (HP)
|
100% |
986
11.7 IPS |
|
|
Face Detection (Q)
|
97% |
2429
29.0 IPS |
|
|
Depth Estimation (SP)
|
100% |
876
6.75 IPS |
|
|
Depth Estimation (HP)
|
99% |
883
6.80 IPS |
|
|
Depth Estimation (Q)
|
64% |
2070
18.8 IPS |
|
|
Style Transfer (SP)
|
100% |
1532
1.97 IPS |
|
|
Style Transfer (HP)
|
100% |
1539
1.98 IPS |
|
|
Style Transfer (Q)
|
98% |
4224
5.45 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
480
17.7 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
489
18.1 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
1758
65.2 IPS |
|
|
Text Classification (SP)
|
100% |
662
884.1 IPS |
|
|
Text Classification (HP)
|
100% |
670
893.6 IPS |
|
|
Text Classification (Q)
|
91% |
1065
1.43 KIPS |
|
|
Machine Translation (SP)
|
100% |
1013
17.5 IPS |
|
|
Machine Translation (HP)
|
100% |
1000
17.2 IPS |
|
|
Machine Translation (Q)
|
57% |
740
18.1 IPS |
