samsung SM-S928B
Geekbench ML Score
1249
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 29 2024 07:49 AM |
| Views | 1 |
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 | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
606
113.4 IPS |
|
|
Image Classification (F16)
|
100% |
608
113.8 IPS |
|
|
Image Classification (I8)
|
97% |
1071
200.4 IPS |
|
|
Image Segmentation (F32)
|
100% |
808
13.5 IPS |
|
|
Image Segmentation (F16)
|
100% |
818
13.7 IPS |
|
|
Image Segmentation (I8)
|
98% |
1000
16.7 IPS |
|
|
Pose Estimation (F32)
|
100% |
1430
1.73 IPS |
|
|
Pose Estimation (F16)
|
100% |
1368
1.66 IPS |
|
|
Pose Estimation (I8)
|
100% |
6135
7.43 IPS |
|
|
Object Detection (F32)
|
100% |
626
46.7 IPS |
|
|
Object Detection (F16)
|
100% |
605
45.1 IPS |
|
|
Object Detection (I8)
|
61% |
1385
103.4 IPS |
|
|
Face Detection (F32)
|
100% |
1410
16.8 IPS |
|
|
Face Detection (F16)
|
100% |
1405
16.7 IPS |
|
|
Face Detection (I8)
|
86% |
2338
27.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
1309
10.2 IPS |
|
|
Depth Estimation (F16)
|
100% |
1244
9.65 IPS |
|
|
Depth Estimation (I8)
|
95% |
2568
19.9 IPS |
|
|
Style Transfer (F32)
|
100% |
2332
3.07 IPS |
|
|
Style Transfer (F16)
|
100% |
2294
3.02 IPS |
|
|
Style Transfer (I8)
|
98% |
5309
6.98 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
894
31.9 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
928
33.1 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2811
100.4 IPS |
|
|
Text Classification (F32)
|
100% |
731
1.05 KIPS |
|
|
Text Classification (F16)
|
100% |
747
1.07 KIPS |
|
|
Text Classification (I8)
|
92% |
544
781.5 IPS |
|
|
Machine Translation (F32)
|
100% |
1497
27.5 IPS |
|
|
Machine Translation (F16)
|
100% |
1422
26.2 IPS |
|
|
Machine Translation (I8)
|
64% |
600
11.0 IPS |
