samsung SM-S928N
Geekbench ML Score
1288
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 09 2024 08:21 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 | 10.81 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
634
118.7 IPS |
|
|
Image Classification (F16)
|
100% |
633
118.4 IPS |
|
|
Image Classification (I8)
|
97% |
1065
199.4 IPS |
|
|
Image Segmentation (F32)
|
100% |
838
14.0 IPS |
|
|
Image Segmentation (F16)
|
100% |
823
13.7 IPS |
|
|
Image Segmentation (I8)
|
98% |
935
15.6 IPS |
|
|
Pose Estimation (F32)
|
100% |
1507
1.83 IPS |
|
|
Pose Estimation (F16)
|
100% |
1418
1.72 IPS |
|
|
Pose Estimation (I8)
|
100% |
6171
7.47 IPS |
|
|
Object Detection (F32)
|
100% |
662
49.4 IPS |
|
|
Object Detection (F16)
|
100% |
640
47.8 IPS |
|
|
Object Detection (I8)
|
61% |
1374
102.6 IPS |
|
|
Face Detection (F32)
|
100% |
1467
17.4 IPS |
|
|
Face Detection (F16)
|
100% |
1449
17.2 IPS |
|
|
Face Detection (I8)
|
86% |
2541
30.2 IPS |
|
|
Depth Estimation (F32)
|
100% |
1450
11.2 IPS |
|
|
Depth Estimation (F16)
|
100% |
1381
10.7 IPS |
|
|
Depth Estimation (I8)
|
95% |
2744
21.3 IPS |
|
|
Style Transfer (F32)
|
100% |
2522
3.32 IPS |
|
|
Style Transfer (F16)
|
100% |
2447
3.22 IPS |
|
|
Style Transfer (I8)
|
98% |
5235
6.89 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
1060
37.8 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
908
32.4 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2582
92.2 IPS |
|
|
Text Classification (F32)
|
100% |
784
1.13 KIPS |
|
|
Text Classification (F16)
|
100% |
850
1.22 KIPS |
|
|
Text Classification (I8)
|
92% |
515
740.3 IPS |
|
|
Machine Translation (F32)
|
100% |
1482
27.3 IPS |
|
|
Machine Translation (F16)
|
100% |
1323
24.4 IPS |
|
|
Machine Translation (I8)
|
64% |
586
10.8 IPS |
