Microsoft Corporation Subsystem for Android(TM)
Geekbench ML Score
511
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 26 2024 02:50 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | Microsoft Corporation Subsystem for Android(TM) |
| Model ID | Microsoft Corporation Subsystem for Android(TM) |
| Motherboard | windows |
| CPU Information | |
|---|---|
| Name | placeholder |
| Topology | 1 Processor, 16 Cores |
| Identifier | ARM implementer 78 architecture 8 variant 2 part 0 revision 1 |
| Base Frequency | 0 MHz |
| Cluster 1 | 16 Cores |
| Memory Information | |
|---|---|
| Size | 5.85 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
535
100.1 IPS |
|
|
Image Classification (F16)
|
100% |
249
46.5 IPS |
|
|
Image Classification (I8)
|
97% |
124
23.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
487
8.14 IPS |
|
|
Image Segmentation (F16)
|
100% |
489
8.17 IPS |
|
|
Image Segmentation (I8)
|
98% |
186
3.10 IPS |
|
|
Pose Estimation (F32)
|
100% |
788
0.95 IPS |
|
|
Pose Estimation (F16)
|
100% |
934
1.13 IPS |
|
|
Pose Estimation (I8)
|
100% |
720
0.87 IPS |
|
|
Object Detection (F32)
|
100% |
473
35.3 IPS |
|
|
Object Detection (F16)
|
100% |
581
43.4 IPS |
|
|
Object Detection (I8)
|
61% |
252
18.8 IPS |
|
|
Face Detection (F32)
|
100% |
894
10.6 IPS |
|
|
Face Detection (F16)
|
100% |
963
11.5 IPS |
|
|
Face Detection (I8)
|
86% |
363
4.31 IPS |
|
|
Depth Estimation (F32)
|
100% |
999
7.75 IPS |
|
|
Depth Estimation (F16)
|
100% |
1003
7.78 IPS |
|
|
Depth Estimation (I8)
|
95% |
528
4.09 IPS |
|
|
Style Transfer (F32)
|
100% |
1368
1.80 IPS |
|
|
Style Transfer (F16)
|
100% |
1370
1.80 IPS |
|
|
Style Transfer (I8)
|
98% |
1610
2.12 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
625
22.3 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
553
19.8 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
290
10.4 IPS |
|
|
Text Classification (F32)
|
100% |
503
722.5 IPS |
|
|
Text Classification (F16)
|
100% |
535
768.6 IPS |
|
|
Text Classification (I8)
|
92% |
91
130.9 IPS |
|
|
Machine Translation (F32)
|
100% |
547
10.1 IPS |
|
|
Machine Translation (F16)
|
100% |
594
10.9 IPS |
|
|
Machine Translation (I8)
|
64% |
172
3.16 IPS |
