samsung SM-S928N
Geekbench ML Score
1360
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | October 18 2024 03:17 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 | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
689
128.9 IPS |
|
|
Image Classification (F16)
|
100% |
765
143.1 IPS |
|
|
Image Classification (I8)
|
99% |
642
120.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
847
14.1 IPS |
|
|
Image Segmentation (F16)
|
100% |
1278
21.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
1222
20.4 IPS |
|
|
Pose Estimation (F32)
|
100% |
1080
1.31 IPS |
|
|
Pose Estimation (F16)
|
100% |
2669
3.23 IPS |
|
|
Pose Estimation (I8)
|
100% |
2678
3.24 IPS |
|
|
Object Detection (F32)
|
100% |
428
31.9 IPS |
|
|
Object Detection (F16)
|
100% |
435
32.5 IPS |
|
|
Object Detection (I8)
|
62% |
270
20.1 IPS |
|
|
Face Detection (F32)
|
100% |
2852
33.9 IPS |
|
|
Face Detection (F16)
|
97% |
3299
39.2 IPS |
|
|
Face Detection (I8)
|
86% |
2989
35.5 IPS |
|
|
Depth Estimation (F32)
|
100% |
1934
15.0 IPS |
|
|
Depth Estimation (F16)
|
100% |
4086
31.7 IPS |
|
|
Depth Estimation (I8)
|
95% |
4059
31.5 IPS |
|
|
Style Transfer (F32)
|
100% |
7247
9.53 IPS |
|
|
Style Transfer (F16)
|
100% |
11810
15.5 IPS |
|
|
Style Transfer (I8)
|
98% |
11763
15.5 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
576
20.6 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1427
51.0 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1460
52.1 IPS |
|
|
Text Classification (F32)
|
100% |
435
625.1 IPS |
|
|
Text Classification (F16)
|
100% |
436
626.3 IPS |
|
|
Text Classification (I8)
|
92% |
712
1.02 KIPS |
|
|
Machine Translation (F32)
|
100% |
707
13.0 IPS |
|
|
Machine Translation (F16)
|
100% |
700
12.9 IPS |
|
|
Machine Translation (I8)
|
64% |
815
15.0 IPS |
