samsung SM-S928B
Geekbench ML Score
1347
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 19 2024 08:24 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 | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
996
186.3 IPS |
|
|
Image Classification (F16)
|
100% |
996
186.4 IPS |
|
|
Image Classification (I8)
|
99% |
655
122.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
955
16.0 IPS |
|
|
Image Segmentation (F16)
|
100% |
1452
24.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
1446
24.1 IPS |
|
|
Pose Estimation (F32)
|
100% |
1081
1.31 IPS |
|
|
Pose Estimation (F16)
|
100% |
2644
3.20 IPS |
|
|
Pose Estimation (I8)
|
100% |
2640
3.20 IPS |
|
|
Object Detection (F32)
|
100% |
375
28.0 IPS |
|
|
Object Detection (F16)
|
100% |
422
31.5 IPS |
|
|
Object Detection (I8)
|
62% |
406
30.3 IPS |
|
|
Face Detection (F32)
|
100% |
2331
27.7 IPS |
|
|
Face Detection (F16)
|
97% |
1862
22.1 IPS |
|
|
Face Detection (I8)
|
86% |
2420
28.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
1878
14.6 IPS |
|
|
Depth Estimation (F16)
|
100% |
3969
30.8 IPS |
|
|
Depth Estimation (I8)
|
95% |
3966
30.8 IPS |
|
|
Style Transfer (F32)
|
100% |
7214
9.49 IPS |
|
|
Style Transfer (F16)
|
100% |
11617
15.3 IPS |
|
|
Style Transfer (I8)
|
98% |
11636
15.3 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
566
20.2 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1039
37.1 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1031
36.8 IPS |
|
|
Text Classification (F32)
|
100% |
450
647.4 IPS |
|
|
Text Classification (F16)
|
100% |
448
643.4 IPS |
|
|
Text Classification (I8)
|
92% |
730
1.05 KIPS |
|
|
Machine Translation (F32)
|
100% |
737
13.6 IPS |
|
|
Machine Translation (F16)
|
100% |
724
13.3 IPS |
|
|
Machine Translation (I8)
|
64% |
837
15.4 IPS |
