motorola motorola edge 40 neo
Geekbench ML Score
475
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 26 2024 09:15 AM |
| 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% |
215
40.3 IPS |
|
|
Image Classification (F16)
|
100% |
219
40.9 IPS |
|
|
Image Classification (I8)
|
97% |
454
84.9 IPS |
|
|
Image Segmentation (F32)
|
100% |
310
5.18 IPS |
|
|
Image Segmentation (F16)
|
100% |
310
5.18 IPS |
|
|
Image Segmentation (I8)
|
98% |
560
9.35 IPS |
|
|
Pose Estimation (F32)
|
100% |
454
0.55 IPS |
|
|
Pose Estimation (F16)
|
100% |
454
0.55 IPS |
|
|
Pose Estimation (I8)
|
100% |
2100
2.54 IPS |
|
|
Object Detection (F32)
|
100% |
244
18.2 IPS |
|
|
Object Detection (F16)
|
100% |
242
18.0 IPS |
|
|
Object Detection (I8)
|
61% |
496
37.1 IPS |
|
|
Face Detection (F32)
|
100% |
473
5.62 IPS |
|
|
Face Detection (F16)
|
100% |
473
5.62 IPS |
|
|
Face Detection (I8)
|
86% |
1154
13.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
487
3.78 IPS |
|
|
Depth Estimation (F16)
|
100% |
489
3.79 IPS |
|
|
Depth Estimation (I8)
|
95% |
1188
9.22 IPS |
|
|
Style Transfer (F32)
|
100% |
830
1.09 IPS |
|
|
Style Transfer (F16)
|
100% |
833
1.10 IPS |
|
|
Style Transfer (I8)
|
98% |
2240
2.95 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
280
10.0 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
281
10.0 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
852
30.4 IPS |
|
|
Text Classification (F32)
|
100% |
295
424.2 IPS |
|
|
Text Classification (F16)
|
100% |
298
428.5 IPS |
|
|
Text Classification (I8)
|
92% |
240
344.9 IPS |
|
|
Machine Translation (F32)
|
100% |
554
10.2 IPS |
|
|
Machine Translation (F16)
|
100% |
541
9.96 IPS |
|
|
Machine Translation (I8)
|
64% |
303
5.57 IPS |
