samsung SM-S928U1
Geekbench ML Score
1148
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | April 27 2024 05:28 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-S928U1 |
| Model ID | samsung SM-S928U1 |
| 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% |
513
96.0 IPS |
|
|
Image Classification (F16)
|
100% |
511
95.6 IPS |
|
|
Image Classification (I8)
|
97% |
945
176.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
675
11.3 IPS |
|
|
Image Segmentation (F16)
|
100% |
673
11.2 IPS |
|
|
Image Segmentation (I8)
|
98% |
821
13.7 IPS |
|
|
Pose Estimation (F32)
|
100% |
1474
1.78 IPS |
|
|
Pose Estimation (F16)
|
100% |
1456
1.76 IPS |
|
|
Pose Estimation (I8)
|
100% |
5793
7.01 IPS |
|
|
Object Detection (F32)
|
100% |
538
40.1 IPS |
|
|
Object Detection (F16)
|
100% |
532
39.7 IPS |
|
|
Object Detection (I8)
|
61% |
1046
78.1 IPS |
|
|
Face Detection (F32)
|
100% |
1239
14.7 IPS |
|
|
Face Detection (F16)
|
100% |
1228
14.6 IPS |
|
|
Face Detection (I8)
|
86% |
2287
27.2 IPS |
|
|
Depth Estimation (F32)
|
100% |
1292
10.0 IPS |
|
|
Depth Estimation (F16)
|
100% |
1260
9.77 IPS |
|
|
Depth Estimation (I8)
|
95% |
2327
18.0 IPS |
|
|
Style Transfer (F32)
|
100% |
2503
3.29 IPS |
|
|
Style Transfer (F16)
|
100% |
2472
3.25 IPS |
|
|
Style Transfer (I8)
|
98% |
5254
6.91 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
922
32.9 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
952
34.0 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2400
85.7 IPS |
|
|
Text Classification (F32)
|
100% |
611
877.6 IPS |
|
|
Text Classification (F16)
|
100% |
633
910.0 IPS |
|
|
Text Classification (I8)
|
92% |
464
666.6 IPS |
|
|
Machine Translation (F32)
|
100% |
1425
26.2 IPS |
|
|
Machine Translation (F16)
|
100% |
1424
26.2 IPS |
|
|
Machine Translation (I8)
|
64% |
566
10.4 IPS |
