samsung SM-S9280
Geekbench ML Score
1368
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | June 28 2025 05:51 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | samsung SM-S9280 |
| Model ID | samsung SM-S9280 |
| 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 | 10.83 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
729
136.5 IPS |
|
|
Image Classification (F16)
|
100% |
798
149.4 IPS |
|
|
Image Classification (I8)
|
99% |
1348
252.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
832
13.9 IPS |
|
|
Image Segmentation (F16)
|
100% |
1260
21.0 IPS |
|
|
Image Segmentation (I8)
|
98% |
1183
19.8 IPS |
|
|
Pose Estimation (F32)
|
100% |
1038
1.26 IPS |
|
|
Pose Estimation (F16)
|
100% |
2353
2.85 IPS |
|
|
Pose Estimation (I8)
|
100% |
2382
2.89 IPS |
|
|
Object Detection (F32)
|
100% |
371
27.7 IPS |
|
|
Object Detection (F16)
|
100% |
431
32.2 IPS |
|
|
Object Detection (I8)
|
62% |
658
49.2 IPS |
|
|
Face Detection (F32)
|
100% |
2907
34.6 IPS |
|
|
Face Detection (F16)
|
97% |
3174
37.7 IPS |
|
|
Face Detection (I8)
|
86% |
3008
35.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
1889
14.6 IPS |
|
|
Depth Estimation (F16)
|
100% |
3718
28.8 IPS |
|
|
Depth Estimation (I8)
|
95% |
4010
31.1 IPS |
|
|
Style Transfer (F32)
|
100% |
6480
8.52 IPS |
|
|
Style Transfer (F16)
|
100% |
10686
14.1 IPS |
|
|
Style Transfer (I8)
|
98% |
10649
14.0 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
447
16.0 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1408
50.3 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1352
48.3 IPS |
|
|
Text Classification (F32)
|
100% |
420
604.2 IPS |
|
|
Text Classification (F16)
|
100% |
431
619.1 IPS |
|
|
Text Classification (I8)
|
92% |
702
1.01 KIPS |
|
|
Machine Translation (F32)
|
100% |
673
12.4 IPS |
|
|
Machine Translation (F16)
|
100% |
651
12.0 IPS |
|
|
Machine Translation (I8)
|
64% |
759
14.0 IPS |
