samsung SM-F956B
Geekbench ML Score
837
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 28 2024 06:23 AM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-F956B |
| Model ID | samsung SM-F956B |
| 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 | 10.86 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
689
128.8 IPS |
|
|
Image Classification (F16)
|
100% |
1133
212.0 IPS |
|
|
Image Classification (I8)
|
99% |
1072
200.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
826
13.8 IPS |
|
|
Image Segmentation (F16)
|
100% |
1286
21.5 IPS |
|
|
Image Segmentation (I8)
|
98% |
1269
21.2 IPS |
|
|
Pose Estimation (F32)
|
100% |
489
0.59 IPS |
|
|
Pose Estimation (F16)
|
100% |
1044
1.26 IPS |
|
|
Pose Estimation (I8)
|
100% |
1037
1.26 IPS |
|
|
Object Detection (F32)
|
100% |
330
24.6 IPS |
|
|
Object Detection (F16)
|
100% |
411
30.7 IPS |
|
|
Object Detection (I8)
|
62% |
406
30.3 IPS |
|
|
Face Detection (F32)
|
100% |
1243
14.8 IPS |
|
|
Face Detection (F16)
|
97% |
2230
26.5 IPS |
|
|
Face Detection (I8)
|
86% |
2133
25.4 IPS |
|
|
Depth Estimation (F32)
|
100% |
801
6.21 IPS |
|
|
Depth Estimation (F16)
|
100% |
1631
12.7 IPS |
|
|
Depth Estimation (I8)
|
95% |
1531
11.9 IPS |
|
|
Style Transfer (F32)
|
100% |
2572
3.38 IPS |
|
|
Style Transfer (F16)
|
100% |
4303
5.66 IPS |
|
|
Style Transfer (I8)
|
98% |
4321
5.68 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
267
9.55 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
734
26.2 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
701
25.0 IPS |
|
|
Text Classification (F32)
|
100% |
247
354.8 IPS |
|
|
Text Classification (F16)
|
100% |
248
356.9 IPS |
|
|
Text Classification (I8)
|
92% |
400
574.9 IPS |
|
|
Machine Translation (F32)
|
100% |
469
8.63 IPS |
|
|
Machine Translation (F16)
|
100% |
411
7.56 IPS |
|
|
Machine Translation (I8)
|
64% |
399
7.34 IPS |
