motorola motorola edge 40
Geekbench ML Score
455
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 18 2024 10:01 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | motorola motorola edge 40 |
| Model ID | motorola motorola edge 40 |
| Motherboard | lyriq |
| CPU Information | |
|---|---|
| Name | ARM MT6891Z_Z/CZA |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 4 Cores @ 2.00 GHz |
| Cluster 2 | 4 Cores @ 2.60 GHz |
| Memory Information | |
|---|---|
| Size | 7.33 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
240
44.8 IPS |
|
|
Image Classification (F16)
|
100% |
218
40.7 IPS |
|
|
Image Classification (I8)
|
97% |
391
73.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
311
5.20 IPS |
|
|
Image Segmentation (F16)
|
100% |
317
5.30 IPS |
|
|
Image Segmentation (I8)
|
98% |
472
7.88 IPS |
|
|
Pose Estimation (F32)
|
100% |
377
0.46 IPS |
|
|
Pose Estimation (F16)
|
100% |
382
0.46 IPS |
|
|
Pose Estimation (I8)
|
100% |
1949
2.36 IPS |
|
|
Object Detection (F32)
|
100% |
240
17.9 IPS |
|
|
Object Detection (F16)
|
100% |
250
18.7 IPS |
|
|
Object Detection (I8)
|
61% |
467
34.9 IPS |
|
|
Face Detection (F32)
|
100% |
575
6.84 IPS |
|
|
Face Detection (F16)
|
100% |
593
7.05 IPS |
|
|
Face Detection (I8)
|
86% |
1079
12.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
513
3.98 IPS |
|
|
Depth Estimation (F16)
|
100% |
513
3.98 IPS |
|
|
Depth Estimation (I8)
|
95% |
901
6.99 IPS |
|
|
Style Transfer (F32)
|
100% |
961
1.26 IPS |
|
|
Style Transfer (F16)
|
100% |
984
1.29 IPS |
|
|
Style Transfer (I8)
|
98% |
2024
2.66 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
293
10.5 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
295
10.5 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
714
25.5 IPS |
|
|
Text Classification (F32)
|
100% |
267
383.3 IPS |
|
|
Text Classification (F16)
|
100% |
267
384.0 IPS |
|
|
Text Classification (I8)
|
92% |
265
381.4 IPS |
|
|
Machine Translation (F32)
|
100% |
497
9.15 IPS |
|
|
Machine Translation (F16)
|
100% |
477
8.79 IPS |
|
|
Machine Translation (I8)
|
64% |
167
3.07 IPS |
