motorola motorola edge 40 neo
Geekbench ML Score
471
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 25 2024 11:42 PM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | motorola motorola edge 40 neo |
| Model ID | motorola motorola edge 40 neo |
| Motherboard | manaus |
| 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 @ 2.50 GHz |
| Memory Information | |
|---|---|
| Size | 11.19 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
213
39.9 IPS |
|
|
Image Classification (F16)
|
100% |
212
39.7 IPS |
|
|
Image Classification (I8)
|
97% |
453
84.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
309
5.15 IPS |
|
|
Image Segmentation (F16)
|
100% |
302
5.05 IPS |
|
|
Image Segmentation (I8)
|
98% |
547
9.14 IPS |
|
|
Pose Estimation (F32)
|
100% |
459
0.56 IPS |
|
|
Pose Estimation (F16)
|
100% |
459
0.56 IPS |
|
|
Pose Estimation (I8)
|
100% |
2077
2.52 IPS |
|
|
Object Detection (F32)
|
100% |
244
18.2 IPS |
|
|
Object Detection (F16)
|
100% |
241
18.0 IPS |
|
|
Object Detection (I8)
|
61% |
485
36.2 IPS |
|
|
Face Detection (F32)
|
100% |
475
5.64 IPS |
|
|
Face Detection (F16)
|
100% |
471
5.60 IPS |
|
|
Face Detection (I8)
|
86% |
1130
13.4 IPS |
|
|
Depth Estimation (F32)
|
100% |
487
3.77 IPS |
|
|
Depth Estimation (F16)
|
100% |
488
3.79 IPS |
|
|
Depth Estimation (I8)
|
95% |
1124
8.71 IPS |
|
|
Style Transfer (F32)
|
100% |
828
1.09 IPS |
|
|
Style Transfer (F16)
|
100% |
824
1.08 IPS |
|
|
Style Transfer (I8)
|
98% |
2220
2.92 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
278
9.93 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
283
10.1 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
843
30.1 IPS |
|
|
Text Classification (F32)
|
100% |
298
428.6 IPS |
|
|
Text Classification (F16)
|
100% |
295
424.0 IPS |
|
|
Text Classification (I8)
|
92% |
236
338.7 IPS |
|
|
Machine Translation (F32)
|
100% |
542
9.98 IPS |
|
|
Machine Translation (F16)
|
100% |
537
9.89 IPS |
|
|
Machine Translation (I8)
|
64% |
304
5.59 IPS |
