samsung SM-S928N
Geekbench ML Score
1200
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 30 2024 01:06 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% |
571
106.8 IPS |
|
|
Image Classification (F16)
|
100% |
562
105.2 IPS |
|
|
Image Classification (I8)
|
97% |
959
179.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
760
12.7 IPS |
|
|
Image Segmentation (F16)
|
100% |
778
13.0 IPS |
|
|
Image Segmentation (I8)
|
98% |
972
16.2 IPS |
|
|
Pose Estimation (F32)
|
100% |
1407
1.70 IPS |
|
|
Pose Estimation (F16)
|
100% |
1386
1.68 IPS |
|
|
Pose Estimation (I8)
|
100% |
5968
7.23 IPS |
|
|
Object Detection (F32)
|
100% |
596
44.5 IPS |
|
|
Object Detection (F16)
|
100% |
586
43.7 IPS |
|
|
Object Detection (I8)
|
61% |
1286
96.0 IPS |
|
|
Face Detection (F32)
|
100% |
1319
15.7 IPS |
|
|
Face Detection (F16)
|
100% |
1327
15.8 IPS |
|
|
Face Detection (I8)
|
86% |
2368
28.2 IPS |
|
|
Depth Estimation (F32)
|
100% |
1294
10.0 IPS |
|
|
Depth Estimation (F16)
|
100% |
1325
10.3 IPS |
|
|
Depth Estimation (I8)
|
95% |
2634
20.4 IPS |
|
|
Style Transfer (F32)
|
100% |
2142
2.82 IPS |
|
|
Style Transfer (F16)
|
100% |
2316
3.05 IPS |
|
|
Style Transfer (I8)
|
98% |
4919
6.47 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
958
34.2 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
931
33.2 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2665
95.2 IPS |
|
|
Text Classification (F32)
|
100% |
656
943.3 IPS |
|
|
Text Classification (F16)
|
100% |
713
1.02 KIPS |
|
|
Text Classification (I8)
|
92% |
526
755.4 IPS |
|
|
Machine Translation (F32)
|
100% |
1339
24.6 IPS |
|
|
Machine Translation (F16)
|
100% |
1275
23.5 IPS |
|
|
Machine Translation (I8)
|
64% |
577
10.6 IPS |
