samsung SM-F956N
Geekbench ML Score
1039
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 26 2024 11:57 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-F956N |
| Model ID | samsung SM-F956N |
| 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 | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
539
100.8 IPS |
|
|
Image Classification (F16)
|
100% |
568
106.2 IPS |
|
|
Image Classification (I8)
|
97% |
979
183.3 IPS |
|
|
Image Segmentation (F32)
|
100% |
778
13.0 IPS |
|
|
Image Segmentation (F16)
|
100% |
766
12.8 IPS |
|
|
Image Segmentation (I8)
|
98% |
930
15.5 IPS |
|
|
Pose Estimation (F32)
|
100% |
1366
1.65 IPS |
|
|
Pose Estimation (F16)
|
100% |
1343
1.63 IPS |
|
|
Pose Estimation (I8)
|
100% |
5379
6.51 IPS |
|
|
Object Detection (F32)
|
100% |
484
36.1 IPS |
|
|
Object Detection (F16)
|
100% |
526
39.2 IPS |
|
|
Object Detection (I8)
|
61% |
1102
82.3 IPS |
|
|
Face Detection (F32)
|
100% |
1153
13.7 IPS |
|
|
Face Detection (F16)
|
100% |
1092
13.0 IPS |
|
|
Face Detection (I8)
|
86% |
2027
24.1 IPS |
|
|
Depth Estimation (F32)
|
100% |
1174
9.10 IPS |
|
|
Depth Estimation (F16)
|
100% |
1179
9.14 IPS |
|
|
Depth Estimation (I8)
|
95% |
1754
13.6 IPS |
|
|
Style Transfer (F32)
|
100% |
2078
2.73 IPS |
|
|
Style Transfer (F16)
|
100% |
2208
2.90 IPS |
|
|
Style Transfer (I8)
|
98% |
4623
6.08 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
689
24.6 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
691
24.7 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1640
58.6 IPS |
|
|
Text Classification (F32)
|
100% |
452
650.2 IPS |
|
|
Text Classification (F16)
|
100% |
526
756.4 IPS |
|
|
Text Classification (I8)
|
92% |
396
569.2 IPS |
|
|
Machine Translation (F32)
|
100% |
1163
21.4 IPS |
|
|
Machine Translation (F16)
|
100% |
1159
21.3 IPS |
|
|
Machine Translation (I8)
|
64% |
529
9.73 IPS |
