samsung SM-F956U1
Geekbench ML Score
444
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 02 2024 12:28 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-F956U1 |
| Model ID | samsung SM-F956U1 |
| 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 | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
295
55.2 IPS |
|
|
Image Classification (F16)
|
100% |
283
52.9 IPS |
|
|
Image Classification (I8)
|
97% |
742
138.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
340
5.68 IPS |
|
|
Image Segmentation (F16)
|
100% |
327
5.46 IPS |
|
|
Image Segmentation (I8)
|
98% |
607
10.1 IPS |
|
|
Pose Estimation (F32)
|
100% |
413
0.50 IPS |
|
|
Pose Estimation (F16)
|
100% |
386
0.47 IPS |
|
|
Pose Estimation (I8)
|
100% |
1335
1.62 IPS |
|
|
Object Detection (F32)
|
100% |
204
15.2 IPS |
|
|
Object Detection (F16)
|
100% |
203
15.2 IPS |
|
|
Object Detection (I8)
|
61% |
539
40.2 IPS |
|
|
Face Detection (F32)
|
100% |
508
6.04 IPS |
|
|
Face Detection (F16)
|
100% |
481
5.72 IPS |
|
|
Face Detection (I8)
|
86% |
992
11.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
383
2.97 IPS |
|
|
Depth Estimation (F16)
|
100% |
389
3.01 IPS |
|
|
Depth Estimation (I8)
|
95% |
1005
7.79 IPS |
|
|
Style Transfer (F32)
|
100% |
631
0.83 IPS |
|
|
Style Transfer (F16)
|
100% |
625
0.82 IPS |
|
|
Style Transfer (I8)
|
98% |
1503
1.98 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
211
7.52 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
209
7.46 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
686
24.5 IPS |
|
|
Text Classification (F32)
|
100% |
243
349.3 IPS |
|
|
Text Classification (F16)
|
100% |
245
351.9 IPS |
|
|
Text Classification (I8)
|
92% |
400
574.9 IPS |
|
|
Machine Translation (F32)
|
100% |
446
8.20 IPS |
|
|
Machine Translation (F16)
|
100% |
425
7.82 IPS |
|
|
Machine Translation (I8)
|
64% |
455
8.37 IPS |
