samsung SM-S9210
Geekbench ML Score
1225
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 15 2024 03:32 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-S9210 |
| Model ID | samsung SM-S9210 |
| 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 | 6.93 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
549
102.6 IPS |
|
|
Image Classification (F16)
|
100% |
590
110.5 IPS |
|
|
Image Classification (I8)
|
97% |
1065
199.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
775
12.9 IPS |
|
|
Image Segmentation (F16)
|
100% |
780
13.0 IPS |
|
|
Image Segmentation (I8)
|
98% |
958
16.0 IPS |
|
|
Pose Estimation (F32)
|
100% |
1257
1.52 IPS |
|
|
Pose Estimation (F16)
|
100% |
1289
1.56 IPS |
|
|
Pose Estimation (I8)
|
100% |
5963
7.22 IPS |
|
|
Object Detection (F32)
|
100% |
612
45.7 IPS |
|
|
Object Detection (F16)
|
100% |
598
44.7 IPS |
|
|
Object Detection (I8)
|
61% |
1399
104.4 IPS |
|
|
Face Detection (F32)
|
100% |
1405
16.7 IPS |
|
|
Face Detection (F16)
|
100% |
1358
16.1 IPS |
|
|
Face Detection (I8)
|
86% |
2408
28.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
1280
9.92 IPS |
|
|
Depth Estimation (F16)
|
100% |
1339
10.4 IPS |
|
|
Depth Estimation (I8)
|
95% |
2653
20.6 IPS |
|
|
Style Transfer (F32)
|
100% |
2287
3.01 IPS |
|
|
Style Transfer (F16)
|
100% |
2284
3.00 IPS |
|
|
Style Transfer (I8)
|
98% |
5335
7.02 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
972
34.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1000
35.7 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2641
94.3 IPS |
|
|
Text Classification (F32)
|
100% |
684
983.5 IPS |
|
|
Text Classification (F16)
|
100% |
750
1.08 KIPS |
|
|
Text Classification (I8)
|
92% |
541
777.0 IPS |
|
|
Machine Translation (F32)
|
100% |
1357
25.0 IPS |
|
|
Machine Translation (F16)
|
100% |
1423
26.2 IPS |
|
|
Machine Translation (I8)
|
64% |
570
10.5 IPS |
