samsung SM-S928N
Geekbench ML Score
665
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 08:20 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-S928N |
| Model ID | samsung SM-S928N |
| 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 | 10.83 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
308
57.7 IPS |
|
|
Image Classification (F16)
|
100% |
304
56.9 IPS |
|
|
Image Classification (I8)
|
97% |
787
147.3 IPS |
|
|
Image Segmentation (F32)
|
100% |
375
6.27 IPS |
|
|
Image Segmentation (F16)
|
100% |
373
6.22 IPS |
|
|
Image Segmentation (I8)
|
98% |
761
12.7 IPS |
|
|
Pose Estimation (F32)
|
100% |
511
0.62 IPS |
|
|
Pose Estimation (F16)
|
100% |
502
0.61 IPS |
|
|
Pose Estimation (I8)
|
100% |
2024
2.45 IPS |
|
|
Object Detection (F32)
|
100% |
317
23.6 IPS |
|
|
Object Detection (F16)
|
100% |
313
23.3 IPS |
|
|
Object Detection (I8)
|
61% |
842
62.9 IPS |
|
|
Face Detection (F32)
|
100% |
745
8.86 IPS |
|
|
Face Detection (F16)
|
100% |
732
8.70 IPS |
|
|
Face Detection (I8)
|
86% |
1738
20.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
643
4.99 IPS |
|
|
Depth Estimation (F16)
|
100% |
635
4.93 IPS |
|
|
Depth Estimation (I8)
|
95% |
1807
14.0 IPS |
|
|
Style Transfer (F32)
|
100% |
1004
1.32 IPS |
|
|
Style Transfer (F16)
|
100% |
999
1.31 IPS |
|
|
Style Transfer (I8)
|
98% |
2516
3.31 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
363
13.0 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
362
12.9 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1231
44.0 IPS |
|
|
Text Classification (F32)
|
100% |
432
620.5 IPS |
|
|
Text Classification (F16)
|
100% |
433
621.7 IPS |
|
|
Text Classification (I8)
|
92% |
693
996.4 IPS |
|
|
Machine Translation (F32)
|
100% |
707
13.0 IPS |
|
|
Machine Translation (F16)
|
100% |
691
12.7 IPS |
|
|
Machine Translation (I8)
|
64% |
809
14.9 IPS |
