samsung SM-S928B
Geekbench ML Score
1165
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | March 26 2025 03:04 PM |
| Views | 7 |
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 | 10.83 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
565
105.8 IPS |
|
|
Image Classification (F16)
|
100% |
647
121.1 IPS |
|
|
Image Classification (I8)
|
99% |
859
160.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
910
15.2 IPS |
|
|
Image Segmentation (F16)
|
100% |
1451
24.2 IPS |
|
|
Image Segmentation (I8)
|
98% |
1572
26.3 IPS |
|
|
Pose Estimation (F32)
|
100% |
892
1.08 IPS |
|
|
Pose Estimation (F16)
|
100% |
1894
2.29 IPS |
|
|
Pose Estimation (I8)
|
100% |
1909
2.31 IPS |
|
|
Object Detection (F32)
|
100% |
418
31.2 IPS |
|
|
Object Detection (F16)
|
100% |
455
33.9 IPS |
|
|
Object Detection (I8)
|
62% |
259
19.3 IPS |
|
|
Face Detection (F32)
|
100% |
2208
26.3 IPS |
|
|
Face Detection (F16)
|
97% |
3307
39.3 IPS |
|
|
Face Detection (I8)
|
86% |
2659
31.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
1467
11.4 IPS |
|
|
Depth Estimation (F16)
|
100% |
2903
22.5 IPS |
|
|
Depth Estimation (I8)
|
95% |
2918
22.6 IPS |
|
|
Style Transfer (F32)
|
100% |
4860
6.39 IPS |
|
|
Style Transfer (F16)
|
100% |
6575
8.65 IPS |
|
|
Style Transfer (I8)
|
98% |
8086
10.6 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
430
15.3 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1303
46.5 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1152
41.1 IPS |
|
|
Text Classification (F32)
|
100% |
367
528.1 IPS |
|
|
Text Classification (F16)
|
100% |
367
527.8 IPS |
|
|
Text Classification (I8)
|
92% |
604
867.7 IPS |
|
|
Machine Translation (F32)
|
100% |
632
11.6 IPS |
|
|
Machine Translation (F16)
|
100% |
636
11.7 IPS |
|
|
Machine Translation (I8)
|
64% |
726
13.4 IPS |
