samsung SM-S928B
Geekbench ML Score
1264
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 10 2024 06:15 AM |
| Views | 1 |
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 | 10.81 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
588
110.0 IPS |
|
|
Image Classification (F16)
|
100% |
604
113.1 IPS |
|
|
Image Classification (I8)
|
97% |
1080
202.0 IPS |
|
|
Image Segmentation (F32)
|
100% |
816
13.6 IPS |
|
|
Image Segmentation (F16)
|
100% |
814
13.6 IPS |
|
|
Image Segmentation (I8)
|
98% |
986
16.5 IPS |
|
|
Pose Estimation (F32)
|
100% |
1441
1.74 IPS |
|
|
Pose Estimation (F16)
|
100% |
1430
1.73 IPS |
|
|
Pose Estimation (I8)
|
100% |
6449
7.81 IPS |
|
|
Object Detection (F32)
|
100% |
621
46.4 IPS |
|
|
Object Detection (F16)
|
100% |
602
44.9 IPS |
|
|
Object Detection (I8)
|
61% |
1311
97.9 IPS |
|
|
Face Detection (F32)
|
100% |
1362
16.2 IPS |
|
|
Face Detection (F16)
|
100% |
1343
16.0 IPS |
|
|
Face Detection (I8)
|
86% |
2539
30.2 IPS |
|
|
Depth Estimation (F32)
|
100% |
1348
10.5 IPS |
|
|
Depth Estimation (F16)
|
100% |
1332
10.3 IPS |
|
|
Depth Estimation (I8)
|
95% |
2707
21.0 IPS |
|
|
Style Transfer (F32)
|
100% |
2388
3.14 IPS |
|
|
Style Transfer (F16)
|
100% |
2371
3.12 IPS |
|
|
Style Transfer (I8)
|
98% |
5159
6.79 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
1013
36.2 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
957
34.2 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2648
94.6 IPS |
|
|
Text Classification (F32)
|
100% |
786
1.13 KIPS |
|
|
Text Classification (F16)
|
100% |
754
1.08 KIPS |
|
|
Text Classification (I8)
|
92% |
547
785.6 IPS |
|
|
Machine Translation (F32)
|
100% |
1452
26.7 IPS |
|
|
Machine Translation (F16)
|
100% |
1396
25.7 IPS |
|
|
Machine Translation (I8)
|
64% |
633
11.7 IPS |
