motorola moto g power - 2025
Geekbench ML Score
330
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | February 01 2025 08:51 PM |
| Views | 7 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | motorola moto g power - 2025 |
| Model ID | motorola moto g power - 2025 |
| Motherboard | vegas |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 4 part 3339 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.40 GHz |
| Memory Information | |
|---|---|
| Size | 7.48 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
135
25.2 IPS |
|
|
Image Classification (F16)
|
100% |
435
81.4 IPS |
|
|
Image Classification (I8)
|
97% |
68
12.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
254
4.24 IPS |
|
|
Image Segmentation (F16)
|
100% |
483
8.06 IPS |
|
|
Image Segmentation (I8)
|
98% |
78
1.29 IPS |
|
|
Pose Estimation (F32)
|
100% |
894
1.08 IPS |
|
|
Pose Estimation (F16)
|
100% |
2075
2.51 IPS |
|
|
Pose Estimation (I8)
|
87% |
1214
1.47 IPS |
|
|
Object Detection (F32)
|
100% |
160
12.0 IPS |
|
|
Object Detection (F16)
|
100% |
345
25.7 IPS |
|
|
Object Detection (I8)
|
51% |
89
6.61 IPS |
|
|
Face Detection (F32)
|
25% |
694
8.26 IPS |
|
|
Face Detection (F16)
|
25% |
1017
12.1 IPS |
|
|
Face Detection (I8)
|
87% |
122
1.45 IPS |
|
|
Depth Estimation (F32)
|
100% |
372
2.89 IPS |
|
|
Depth Estimation (F16)
|
74% |
475
3.68 IPS |
|
|
Depth Estimation (I8)
|
93% |
349
2.71 IPS |
|
|
Style Transfer (F32)
|
100% |
723
0.95 IPS |
|
|
Style Transfer (F16)
|
100% |
1288
1.69 IPS |
|
|
Style Transfer (I8)
|
98% |
1687
2.22 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
380
13.6 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
904
32.3 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
467
16.7 IPS |
|
|
Text Classification (F32)
|
100% |
87
124.9 IPS |
|
|
Text Classification (F16)
|
100% |
106
151.8 IPS |
|
|
Text Classification (I8)
|
94% |
85
122.8 IPS |
|
|
Machine Translation (F32)
|
100% |
231
4.25 IPS |
|
|
Machine Translation (F16)
|
100% |
238
4.38 IPS |
|
|
Machine Translation (I8)
|
57% |
157
2.90 IPS |
