motorola moto g power 5G - 2024
Geekbench ML Score
438
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 26 2024 11:42 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 | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
195
36.4 IPS |
|
|
Image Classification (F16)
|
100% |
193
36.1 IPS |
|
|
Image Classification (I8)
|
97% |
403
75.4 IPS |
|
|
Image Segmentation (F32)
|
100% |
296
4.95 IPS |
|
|
Image Segmentation (F16)
|
100% |
299
4.99 IPS |
|
|
Image Segmentation (I8)
|
98% |
496
8.29 IPS |
|
|
Pose Estimation (F32)
|
100% |
416
0.50 IPS |
|
|
Pose Estimation (F16)
|
100% |
408
0.49 IPS |
|
|
Pose Estimation (I8)
|
100% |
2012
2.44 IPS |
|
|
Object Detection (F32)
|
100% |
225
16.8 IPS |
|
|
Object Detection (F16)
|
100% |
228
17.0 IPS |
|
|
Object Detection (I8)
|
61% |
430
32.1 IPS |
|
|
Face Detection (F32)
|
100% |
460
5.47 IPS |
|
|
Face Detection (F16)
|
100% |
464
5.52 IPS |
|
|
Face Detection (I8)
|
86% |
1065
12.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
454
3.52 IPS |
|
|
Depth Estimation (F16)
|
100% |
455
3.53 IPS |
|
|
Depth Estimation (I8)
|
95% |
1025
7.95 IPS |
|
|
Style Transfer (F32)
|
100% |
751
0.99 IPS |
|
|
Style Transfer (F16)
|
100% |
763
1.00 IPS |
|
|
Style Transfer (I8)
|
98% |
2128
2.80 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
267
9.53 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
262
9.37 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
704
25.1 IPS |
|
|
Text Classification (F32)
|
100% |
260
373.4 IPS |
|
|
Text Classification (F16)
|
100% |
253
363.5 IPS |
|
|
Text Classification (I8)
|
92% |
225
323.0 IPS |
|
|
Machine Translation (F32)
|
100% |
510
9.38 IPS |
|
|
Machine Translation (F16)
|
100% |
518
9.54 IPS |
|
|
Machine Translation (I8)
|
64% |
310
5.71 IPS |
