samsung SM-S928B
Geekbench ML Score
1318
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | May 01 2024 09:23 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | samsung SM-S928B |
| Model ID | samsung SM-S928B |
| 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% |
567
106.1 IPS |
|
|
Image Classification (F16)
|
100% |
630
117.9 IPS |
|
|
Image Classification (I8)
|
97% |
1074
200.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
824
13.8 IPS |
|
|
Image Segmentation (F16)
|
100% |
823
13.7 IPS |
|
|
Image Segmentation (I8)
|
98% |
1004
16.8 IPS |
|
|
Pose Estimation (F32)
|
100% |
1516
1.84 IPS |
|
|
Pose Estimation (F16)
|
100% |
1477
1.79 IPS |
|
|
Pose Estimation (I8)
|
100% |
6445
7.80 IPS |
|
|
Object Detection (F32)
|
100% |
613
45.8 IPS |
|
|
Object Detection (F16)
|
100% |
648
48.4 IPS |
|
|
Object Detection (I8)
|
61% |
1448
108.1 IPS |
|
|
Face Detection (F32)
|
100% |
1481
17.6 IPS |
|
|
Face Detection (F16)
|
100% |
1462
17.4 IPS |
|
|
Face Detection (I8)
|
86% |
2545
30.3 IPS |
|
|
Depth Estimation (F32)
|
100% |
1508
11.7 IPS |
|
|
Depth Estimation (F16)
|
100% |
1458
11.3 IPS |
|
|
Depth Estimation (I8)
|
95% |
2790
21.6 IPS |
|
|
Style Transfer (F32)
|
100% |
2525
3.32 IPS |
|
|
Style Transfer (F16)
|
100% |
2512
3.30 IPS |
|
|
Style Transfer (I8)
|
98% |
5309
6.98 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
1067
38.1 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
1108
39.6 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
2930
104.6 IPS |
|
|
Text Classification (F32)
|
100% |
764
1.10 KIPS |
|
|
Text Classification (F16)
|
100% |
769
1.11 KIPS |
|
|
Text Classification (I8)
|
92% |
562
807.5 IPS |
|
|
Machine Translation (F32)
|
100% |
1494
27.5 IPS |
|
|
Machine Translation (F16)
|
100% |
1507
27.7 IPS |
|
|
Machine Translation (I8)
|
64% |
629
11.6 IPS |
