samsung SM-S928B
Geekbench ML Score
1594
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 27 2024 11:32 PM |
| 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 | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
668
125.0 IPS |
|
|
Image Classification (F16)
|
100% |
1051
196.7 IPS |
|
|
Image Classification (I8)
|
99% |
850
159.1 IPS |
|
|
Image Segmentation (F32)
|
100% |
1373
22.9 IPS |
|
|
Image Segmentation (F16)
|
100% |
2869
47.9 IPS |
|
|
Image Segmentation (I8)
|
98% |
2436
40.7 IPS |
|
|
Pose Estimation (F32)
|
100% |
1082
1.31 IPS |
|
|
Pose Estimation (F16)
|
100% |
2677
3.24 IPS |
|
|
Pose Estimation (I8)
|
100% |
2677
3.24 IPS |
|
|
Object Detection (F32)
|
100% |
604
45.1 IPS |
|
|
Object Detection (F16)
|
100% |
537
40.1 IPS |
|
|
Object Detection (I8)
|
62% |
482
36.0 IPS |
|
|
Face Detection (F32)
|
100% |
2845
33.8 IPS |
|
|
Face Detection (F16)
|
97% |
4666
55.5 IPS |
|
|
Face Detection (I8)
|
86% |
3949
46.9 IPS |
|
|
Depth Estimation (F32)
|
100% |
1911
14.8 IPS |
|
|
Depth Estimation (F16)
|
100% |
4099
31.8 IPS |
|
|
Depth Estimation (I8)
|
95% |
4060
31.5 IPS |
|
|
Style Transfer (F32)
|
100% |
7268
9.56 IPS |
|
|
Style Transfer (F16)
|
100% |
11789
15.5 IPS |
|
|
Style Transfer (I8)
|
98% |
11770
15.5 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
573
20.5 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1819
65.0 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1786
63.8 IPS |
|
|
Text Classification (F32)
|
100% |
436
626.2 IPS |
|
|
Text Classification (F16)
|
100% |
433
621.7 IPS |
|
|
Text Classification (I8)
|
92% |
712
1.02 KIPS |
|
|
Machine Translation (F32)
|
100% |
714
13.1 IPS |
|
|
Machine Translation (F16)
|
100% |
706
13.0 IPS |
|
|
Machine Translation (I8)
|
64% |
817
15.0 IPS |
