samsung SM-S928B
Geekbench ML Score
1264
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | April 27 2024 08:34 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% |
584
109.3 IPS |
|
|
Image Classification (F16)
|
100% |
581
108.8 IPS |
|
|
Image Classification (I8)
|
97% |
982
183.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
827
13.8 IPS |
|
|
Image Segmentation (F16)
|
100% |
777
13.0 IPS |
|
|
Image Segmentation (I8)
|
98% |
1028
17.2 IPS |
|
|
Pose Estimation (F32)
|
100% |
1443
1.75 IPS |
|
|
Pose Estimation (F16)
|
100% |
1400
1.69 IPS |
|
|
Pose Estimation (I8)
|
100% |
6173
7.47 IPS |
|
|
Object Detection (F32)
|
100% |
599
44.7 IPS |
|
|
Object Detection (F16)
|
100% |
614
45.8 IPS |
|
|
Object Detection (I8)
|
61% |
1239
92.5 IPS |
|
|
Face Detection (F32)
|
100% |
1398
16.6 IPS |
|
|
Face Detection (F16)
|
100% |
1425
16.9 IPS |
|
|
Face Detection (I8)
|
86% |
2592
30.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
1345
10.4 IPS |
|
|
Depth Estimation (F16)
|
100% |
1372
10.6 IPS |
|
|
Depth Estimation (I8)
|
95% |
2699
20.9 IPS |
|
|
Style Transfer (F32)
|
100% |
2458
3.23 IPS |
|
|
Style Transfer (F16)
|
100% |
2444
3.21 IPS |
|
|
Style Transfer (I8)
|
98% |
5477
7.20 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
975
34.8 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
982
35.1 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2810
100.4 IPS |
|
|
Text Classification (F32)
|
100% |
731
1.05 KIPS |
|
|
Text Classification (F16)
|
100% |
750
1.08 KIPS |
|
|
Text Classification (I8)
|
92% |
555
797.7 IPS |
|
|
Machine Translation (F32)
|
100% |
1470
27.1 IPS |
|
|
Machine Translation (F16)
|
100% |
1432
26.3 IPS |
|
|
Machine Translation (I8)
|
64% |
624
11.5 IPS |
