samsung SM-S928U
Geekbench ML Score
1043
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | April 27 2025 09:23 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | samsung SM-S928U |
| Model ID | samsung SM-S928U |
| 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 | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
584
109.2 IPS |
|
|
Image Classification (F16)
|
100% |
515
96.4 IPS |
|
|
Image Classification (I8)
|
97% |
950
177.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
621
10.4 IPS |
|
|
Image Segmentation (F16)
|
100% |
618
10.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
701
11.7 IPS |
|
|
Pose Estimation (F32)
|
100% |
1361
1.65 IPS |
|
|
Pose Estimation (F16)
|
100% |
1321
1.60 IPS |
|
|
Pose Estimation (I8)
|
100% |
6025
7.30 IPS |
|
|
Object Detection (F32)
|
100% |
296
22.1 IPS |
|
|
Object Detection (F16)
|
100% |
353
26.3 IPS |
|
|
Object Detection (I8)
|
61% |
909
67.8 IPS |
|
|
Face Detection (F32)
|
100% |
1022
12.2 IPS |
|
|
Face Detection (F16)
|
100% |
1078
12.8 IPS |
|
|
Face Detection (I8)
|
86% |
1566
18.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
1213
9.41 IPS |
|
|
Depth Estimation (F16)
|
100% |
1228
9.52 IPS |
|
|
Depth Estimation (I8)
|
95% |
2310
17.9 IPS |
|
|
Style Transfer (F32)
|
100% |
2360
3.10 IPS |
|
|
Style Transfer (F16)
|
100% |
2377
3.13 IPS |
|
|
Style Transfer (I8)
|
98% |
5055
6.65 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
871
31.1 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
983
35.1 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2471
88.3 IPS |
|
|
Text Classification (F32)
|
100% |
715
1.03 KIPS |
|
|
Text Classification (F16)
|
100% |
678
974.6 IPS |
|
|
Text Classification (I8)
|
92% |
573
823.7 IPS |
|
|
Machine Translation (F32)
|
100% |
1000
18.4 IPS |
|
|
Machine Translation (F16)
|
100% |
958
17.6 IPS |
|
|
Machine Translation (I8)
|
64% |
477
8.78 IPS |
