samsung SM-S9210
Geekbench ML Score
1391
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 15 2024 03:39 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 | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
414
77.5 IPS |
|
|
Image Classification (F16)
|
100% |
1067
199.6 IPS |
|
|
Image Classification (I8)
|
99% |
734
137.4 IPS |
|
|
Image Segmentation (F32)
|
100% |
824
13.8 IPS |
|
|
Image Segmentation (F16)
|
100% |
1137
19.0 IPS |
|
|
Image Segmentation (I8)
|
98% |
1382
23.1 IPS |
|
|
Pose Estimation (F32)
|
100% |
1072
1.30 IPS |
|
|
Pose Estimation (F16)
|
100% |
2418
2.93 IPS |
|
|
Pose Estimation (I8)
|
100% |
2682
3.25 IPS |
|
|
Object Detection (F32)
|
100% |
540
40.3 IPS |
|
|
Object Detection (F16)
|
100% |
567
42.3 IPS |
|
|
Object Detection (I8)
|
62% |
669
50.0 IPS |
|
|
Face Detection (F32)
|
100% |
3047
36.2 IPS |
|
|
Face Detection (F16)
|
97% |
5358
63.7 IPS |
|
|
Face Detection (I8)
|
86% |
3090
36.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
1775
13.8 IPS |
|
|
Depth Estimation (F16)
|
100% |
3763
29.2 IPS |
|
|
Depth Estimation (I8)
|
95% |
3788
29.4 IPS |
|
|
Style Transfer (F32)
|
100% |
6854
9.02 IPS |
|
|
Style Transfer (F16)
|
100% |
11114
14.6 IPS |
|
|
Style Transfer (I8)
|
98% |
11077
14.6 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
467
16.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1341
47.9 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1115
39.8 IPS |
|
|
Text Classification (F32)
|
100% |
423
608.0 IPS |
|
|
Text Classification (F16)
|
100% |
434
624.2 IPS |
|
|
Text Classification (I8)
|
92% |
695
999.3 IPS |
|
|
Machine Translation (F32)
|
100% |
677
12.5 IPS |
|
|
Machine Translation (F16)
|
100% |
666
12.3 IPS |
|
|
Machine Translation (I8)
|
64% |
784
14.4 IPS |
