samsung SM-F966B
Geekbench AI Score
578
Single Precision Score
567
Half Precision Score
1215
Quantized Score
Geekbench AI 1.4.0 for Android AArch64
Result Information
| User | Molpid |
| Upload Date | August 06 2025 09:29 PM |
| Views | 8 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Qualcomm ARMv8 |
| System Information | |
|---|---|
| Operating System | Android 16 |
| Model | samsung SM-F966B |
| Model ID | samsung SM-F966B |
| 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 | 14.75 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
430
80.0 IPS |
|
|
Image Classification (HP)
|
100% |
425
79.1 IPS |
|
|
Image Classification (Q)
|
99% |
887
165.5 IPS |
|
|
Image Segmentation (SP)
|
100% |
420
6.80 IPS |
|
|
Image Segmentation (HP)
|
100% |
432
7.00 IPS |
|
|
Image Segmentation (Q)
|
98% |
823
13.4 IPS |
|
|
Pose Estimation (SP)
|
100% |
611
0.71 IPS |
|
|
Pose Estimation (HP)
|
100% |
584
0.68 IPS |
|
|
Pose Estimation (Q)
|
98% |
2336
2.73 IPS |
|
|
Object Detection (SP)
|
100% |
334
26.5 IPS |
|
|
Object Detection (HP)
|
100% |
335
26.5 IPS |
|
|
Object Detection (Q)
|
87% |
907
72.8 IPS |
|
|
Face Detection (SP)
|
100% |
855
10.2 IPS |
|
|
Face Detection (HP)
|
100% |
857
10.2 IPS |
|
|
Face Detection (Q)
|
97% |
1807
21.6 IPS |
|
|
Depth Estimation (SP)
|
100% |
717
5.53 IPS |
|
|
Depth Estimation (HP)
|
99% |
658
5.07 IPS |
|
|
Depth Estimation (Q)
|
64% |
1550
14.1 IPS |
|
|
Style Transfer (SP)
|
100% |
1258
1.62 IPS |
|
|
Style Transfer (HP)
|
100% |
1168
1.50 IPS |
|
|
Style Transfer (Q)
|
98% |
3299
4.25 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
398
14.7 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
402
14.8 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
1287
47.7 IPS |
|
|
Text Classification (SP)
|
100% |
442
589.8 IPS |
|
|
Text Classification (HP)
|
100% |
467
622.7 IPS |
|
|
Text Classification (Q)
|
91% |
716
962.4 IPS |
|
|
Machine Translation (SP)
|
100% |
836
14.4 IPS |
|
|
Machine Translation (HP)
|
100% |
776
13.4 IPS |
|
|
Machine Translation (Q)
|
57% |
536
13.1 IPS |
