samsung SM-S926U
Geekbench ML Score
1296
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 07 2024 02:23 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-S926U |
| Model ID | samsung SM-S926U |
| 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% |
694
129.8 IPS |
|
|
Image Classification (F16)
|
100% |
699
130.8 IPS |
|
|
Image Classification (I8)
|
99% |
735
137.6 IPS |
|
|
Image Segmentation (F32)
|
100% |
826
13.8 IPS |
|
|
Image Segmentation (F16)
|
100% |
1157
19.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
995
16.6 IPS |
|
|
Pose Estimation (F32)
|
100% |
1084
1.31 IPS |
|
|
Pose Estimation (F16)
|
100% |
2655
3.21 IPS |
|
|
Pose Estimation (I8)
|
100% |
2694
3.26 IPS |
|
|
Object Detection (F32)
|
100% |
257
19.2 IPS |
|
|
Object Detection (F16)
|
100% |
421
31.4 IPS |
|
|
Object Detection (I8)
|
62% |
447
33.4 IPS |
|
|
Face Detection (F32)
|
100% |
2111
25.1 IPS |
|
|
Face Detection (F16)
|
97% |
2680
31.9 IPS |
|
|
Face Detection (I8)
|
86% |
2652
31.5 IPS |
|
|
Depth Estimation (F32)
|
100% |
1908
14.8 IPS |
|
|
Depth Estimation (F16)
|
100% |
3983
30.9 IPS |
|
|
Depth Estimation (I8)
|
95% |
3935
30.5 IPS |
|
|
Style Transfer (F32)
|
100% |
7251
9.54 IPS |
|
|
Style Transfer (F16)
|
100% |
11670
15.4 IPS |
|
|
Style Transfer (I8)
|
98% |
11707
15.4 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
566
20.2 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1077
38.5 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1142
40.8 IPS |
|
|
Text Classification (F32)
|
100% |
447
641.9 IPS |
|
|
Text Classification (F16)
|
100% |
445
640.1 IPS |
|
|
Text Classification (I8)
|
92% |
724
1.04 KIPS |
|
|
Machine Translation (F32)
|
100% |
727
13.4 IPS |
|
|
Machine Translation (F16)
|
100% |
712
13.1 IPS |
|
|
Machine Translation (I8)
|
64% |
827
15.2 IPS |
