motorola moto g power 5G - 2024
Geekbench ML Score
267
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 26 2024 11:54 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | motorola moto g power 5G - 2024 |
| Model ID | motorola moto g power 5G - 2024 |
| Motherboard | cancunn |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.20 GHz |
| Memory Information | |
|---|---|
| Size | 7.44 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
180
33.7 IPS |
|
|
Image Classification (F16)
|
97% |
341
63.8 IPS |
|
|
Image Classification (I8)
|
97% |
72
13.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
244
4.08 IPS |
|
|
Image Segmentation (F16)
|
100% |
624
10.4 IPS |
|
|
Image Segmentation (I8)
|
98% |
84
1.40 IPS |
|
|
Pose Estimation (F32)
|
100% |
995
1.21 IPS |
|
|
Pose Estimation (F16)
|
100% |
2168
2.63 IPS |
|
|
Pose Estimation (I8)
|
80% |
1205
1.46 IPS |
|
|
Object Detection (F32)
|
100% |
177
13.2 IPS |
|
|
Object Detection (F16)
|
100% |
326
24.4 IPS |
|
|
Object Detection (I8)
|
51% |
94
7.00 IPS |
|
|
Face Detection (F32)
|
100% |
600
7.13 IPS |
|
|
Face Detection (F16)
|
97% |
1146
13.6 IPS |
|
|
Face Detection (I8)
|
87% |
130
1.55 IPS |
|
|
Depth Estimation (F32)
|
98% |
667
5.17 IPS |
|
|
Depth Estimation (F16)
|
98% |
1497
11.6 IPS |
|
|
Depth Estimation (I8)
|
93% |
374
2.90 IPS |
|
|
Style Transfer (F32)
|
99% |
881
1.16 IPS |
|
|
Style Transfer (F16)
|
99% |
1218
1.60 IPS |
|
|
Style Transfer (I8)
|
98% |
1784
2.35 IPS |
|
|
Image Super-Resolution (F32)
|
98% |
438
15.7 IPS |
|
|
Image Super-Resolution (F16)
|
0
|
||
|
Image Super-Resolution (I8)
|
98% |
501
17.9 IPS |
|
|
Text Classification (F32)
|
36% |
40
57.4 IPS |
|
|
Text Classification (F16)
|
36% |
36
52.1 IPS |
|
|
Text Classification (I8)
|
94% |
70
101.2 IPS |
|
|
Machine Translation (F32)
|
100% |
153
2.81 IPS |
|
|
Machine Translation (F16)
|
100% |
247
4.54 IPS |
|
|
Machine Translation (I8)
|
57% |
153
2.82 IPS |
