samsung SM-S928B
Geekbench ML Score
436
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 09 2024 08:05 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 | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
283
52.9 IPS |
|
|
Image Classification (F16)
|
100% |
279
52.3 IPS |
|
|
Image Classification (I8)
|
97% |
719
134.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
304
5.08 IPS |
|
|
Image Segmentation (F16)
|
100% |
298
4.98 IPS |
|
|
Image Segmentation (I8)
|
98% |
546
9.13 IPS |
|
|
Pose Estimation (F32)
|
100% |
351
0.43 IPS |
|
|
Pose Estimation (F16)
|
100% |
335
0.41 IPS |
|
|
Pose Estimation (I8)
|
100% |
1175
1.42 IPS |
|
|
Object Detection (F32)
|
100% |
184
13.7 IPS |
|
|
Object Detection (F16)
|
100% |
184
13.7 IPS |
|
|
Object Detection (I8)
|
61% |
487
36.4 IPS |
|
|
Face Detection (F32)
|
100% |
460
5.47 IPS |
|
|
Face Detection (F16)
|
100% |
466
5.54 IPS |
|
|
Face Detection (I8)
|
86% |
1014
12.1 IPS |
|
|
Depth Estimation (F32)
|
100% |
413
3.20 IPS |
|
|
Depth Estimation (F16)
|
100% |
416
3.23 IPS |
|
|
Depth Estimation (I8)
|
95% |
1088
8.44 IPS |
|
|
Style Transfer (F32)
|
100% |
666
0.88 IPS |
|
|
Style Transfer (F16)
|
100% |
660
0.87 IPS |
|
|
Style Transfer (I8)
|
98% |
1573
2.07 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
221
7.88 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
221
7.91 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
735
26.2 IPS |
|
|
Text Classification (F32)
|
100% |
245
352.8 IPS |
|
|
Text Classification (F16)
|
100% |
246
354.1 IPS |
|
|
Text Classification (I8)
|
92% |
401
577.0 IPS |
|
|
Machine Translation (F32)
|
100% |
473
8.70 IPS |
|
|
Machine Translation (F16)
|
100% |
455
8.37 IPS |
|
|
Machine Translation (I8)
|
64% |
474
8.72 IPS |
