samsung SM-S928B
Geekbench ML Score
1306
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 19 2024 06:55 AM |
| Views | 2 |
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% |
598
111.9 IPS |
|
|
Image Classification (F16)
|
100% |
642
120.1 IPS |
|
|
Image Classification (I8)
|
97% |
1064
199.1 IPS |
|
|
Image Segmentation (F32)
|
100% |
840
14.0 IPS |
|
|
Image Segmentation (F16)
|
100% |
857
14.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
972
16.2 IPS |
|
|
Pose Estimation (F32)
|
100% |
1492
1.81 IPS |
|
|
Pose Estimation (F16)
|
100% |
1439
1.74 IPS |
|
|
Pose Estimation (I8)
|
100% |
6277
7.60 IPS |
|
|
Object Detection (F32)
|
100% |
638
47.6 IPS |
|
|
Object Detection (F16)
|
100% |
637
47.6 IPS |
|
|
Object Detection (I8)
|
61% |
1411
105.4 IPS |
|
|
Face Detection (F32)
|
100% |
1492
17.7 IPS |
|
|
Face Detection (F16)
|
100% |
1465
17.4 IPS |
|
|
Face Detection (I8)
|
86% |
2576
30.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
1438
11.2 IPS |
|
|
Depth Estimation (F16)
|
100% |
1453
11.3 IPS |
|
|
Depth Estimation (I8)
|
95% |
2724
21.1 IPS |
|
|
Style Transfer (F32)
|
100% |
2461
3.24 IPS |
|
|
Style Transfer (F16)
|
100% |
2554
3.36 IPS |
|
|
Style Transfer (I8)
|
98% |
5192
6.83 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
998
35.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1065
38.0 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2738
97.8 IPS |
|
|
Text Classification (F32)
|
100% |
729
1.05 KIPS |
|
|
Text Classification (F16)
|
100% |
771
1.11 KIPS |
|
|
Text Classification (I8)
|
92% |
554
795.6 IPS |
|
|
Machine Translation (F32)
|
100% |
1551
28.5 IPS |
|
|
Machine Translation (F16)
|
100% |
1538
28.3 IPS |
|
|
Machine Translation (I8)
|
64% |
615
11.3 IPS |
