samsung SM-S928N
Geekbench ML Score
1293
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 09 2024 08:29 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 | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
698
130.7 IPS |
|
|
Image Classification (F16)
|
100% |
800
149.6 IPS |
|
|
Image Classification (I8)
|
99% |
336
62.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
984
16.4 IPS |
|
|
Image Segmentation (F16)
|
100% |
1493
24.9 IPS |
|
|
Image Segmentation (I8)
|
98% |
1417
23.7 IPS |
|
|
Pose Estimation (F32)
|
100% |
1086
1.32 IPS |
|
|
Pose Estimation (F16)
|
100% |
2672
3.24 IPS |
|
|
Pose Estimation (I8)
|
100% |
2658
3.22 IPS |
|
|
Object Detection (F32)
|
100% |
287
21.4 IPS |
|
|
Object Detection (F16)
|
100% |
436
32.6 IPS |
|
|
Object Detection (I8)
|
62% |
271
20.2 IPS |
|
|
Face Detection (F32)
|
100% |
2875
34.2 IPS |
|
|
Face Detection (F16)
|
97% |
2661
31.6 IPS |
|
|
Face Detection (I8)
|
86% |
2336
27.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
1896
14.7 IPS |
|
|
Depth Estimation (F16)
|
100% |
4121
32.0 IPS |
|
|
Depth Estimation (I8)
|
95% |
3977
30.8 IPS |
|
|
Style Transfer (F32)
|
100% |
7253
9.54 IPS |
|
|
Style Transfer (F16)
|
100% |
11698
15.4 IPS |
|
|
Style Transfer (I8)
|
98% |
11684
15.4 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
546
19.5 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
996
35.6 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1211
43.2 IPS |
|
|
Text Classification (F32)
|
100% |
445
639.7 IPS |
|
|
Text Classification (F16)
|
100% |
445
639.3 IPS |
|
|
Text Classification (I8)
|
92% |
719
1.03 KIPS |
|
|
Machine Translation (F32)
|
100% |
724
13.3 IPS |
|
|
Machine Translation (F16)
|
100% |
713
13.1 IPS |
|
|
Machine Translation (I8)
|
64% |
831
15.3 IPS |
