samsung SM-S928N
Geekbench ML Score
668
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | February 23 2024 02:54 AM |
| Views | 2 |
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 | 10.81 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
305
57.0 IPS |
|
|
Image Classification (F16)
|
100% |
301
56.4 IPS |
|
|
Image Classification (I8)
|
97% |
799
149.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
374
6.24 IPS |
|
|
Image Segmentation (F16)
|
100% |
371
6.19 IPS |
|
|
Image Segmentation (I8)
|
98% |
763
12.7 IPS |
|
|
Pose Estimation (F32)
|
100% |
508
0.61 IPS |
|
|
Pose Estimation (F16)
|
100% |
508
0.61 IPS |
|
|
Pose Estimation (I8)
|
100% |
1980
2.40 IPS |
|
|
Object Detection (F32)
|
100% |
313
23.4 IPS |
|
|
Object Detection (F16)
|
100% |
310
23.1 IPS |
|
|
Object Detection (I8)
|
61% |
852
63.6 IPS |
|
|
Face Detection (F32)
|
100% |
726
8.63 IPS |
|
|
Face Detection (F16)
|
100% |
722
8.59 IPS |
|
|
Face Detection (I8)
|
86% |
1727
20.5 IPS |
|
|
Depth Estimation (F32)
|
100% |
657
5.10 IPS |
|
|
Depth Estimation (F16)
|
100% |
656
5.09 IPS |
|
|
Depth Estimation (I8)
|
95% |
1838
14.3 IPS |
|
|
Style Transfer (F32)
|
100% |
1036
1.36 IPS |
|
|
Style Transfer (F16)
|
100% |
1030
1.35 IPS |
|
|
Style Transfer (I8)
|
98% |
2540
3.34 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
362
12.9 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
364
13.0 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1249
44.6 IPS |
|
|
Text Classification (F32)
|
100% |
429
616.1 IPS |
|
|
Text Classification (F16)
|
100% |
434
623.1 IPS |
|
|
Text Classification (I8)
|
92% |
706
1.02 KIPS |
|
|
Machine Translation (F32)
|
100% |
716
13.2 IPS |
|
|
Machine Translation (F16)
|
100% |
699
12.9 IPS |
|
|
Machine Translation (I8)
|
64% |
817
15.0 IPS |
