samsung SM-S926U
Geekbench ML Score
1165
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | December 09 2024 12:07 AM |
| Views | 5 |
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 | 10.83 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
522
97.7 IPS |
|
|
Image Classification (F16)
|
100% |
525
98.2 IPS |
|
|
Image Classification (I8)
|
97% |
946
176.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
690
11.5 IPS |
|
|
Image Segmentation (F16)
|
100% |
702
11.7 IPS |
|
|
Image Segmentation (I8)
|
98% |
828
13.8 IPS |
|
|
Pose Estimation (F32)
|
100% |
1467
1.78 IPS |
|
|
Pose Estimation (F16)
|
100% |
1468
1.78 IPS |
|
|
Pose Estimation (I8)
|
100% |
5763
6.98 IPS |
|
|
Object Detection (F32)
|
100% |
540
40.4 IPS |
|
|
Object Detection (F16)
|
100% |
539
40.2 IPS |
|
|
Object Detection (I8)
|
61% |
1065
79.5 IPS |
|
|
Face Detection (F32)
|
100% |
1265
15.0 IPS |
|
|
Face Detection (F16)
|
100% |
1272
15.1 IPS |
|
|
Face Detection (I8)
|
86% |
2242
26.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
1285
9.97 IPS |
|
|
Depth Estimation (F16)
|
100% |
1338
10.4 IPS |
|
|
Depth Estimation (I8)
|
95% |
2454
19.0 IPS |
|
|
Style Transfer (F32)
|
100% |
2507
3.30 IPS |
|
|
Style Transfer (F16)
|
100% |
2510
3.30 IPS |
|
|
Style Transfer (I8)
|
98% |
5226
6.87 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
1029
36.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
938
33.5 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2368
84.6 IPS |
|
|
Text Classification (F32)
|
100% |
616
884.7 IPS |
|
|
Text Classification (F16)
|
100% |
601
863.3 IPS |
|
|
Text Classification (I8)
|
92% |
480
689.6 IPS |
|
|
Machine Translation (F32)
|
100% |
1451
26.7 IPS |
|
|
Machine Translation (F16)
|
100% |
1466
27.0 IPS |
|
|
Machine Translation (I8)
|
64% |
579
10.6 IPS |
