motorola motorola edge 40 neo
Geekbench ML Score
474
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 25 2024 09:24 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% |
216
40.4 IPS |
|
|
Image Classification (F16)
|
100% |
219
41.0 IPS |
|
|
Image Classification (I8)
|
97% |
444
83.1 IPS |
|
|
Image Segmentation (F32)
|
100% |
311
5.20 IPS |
|
|
Image Segmentation (F16)
|
100% |
308
5.14 IPS |
|
|
Image Segmentation (I8)
|
98% |
550
9.19 IPS |
|
|
Pose Estimation (F32)
|
100% |
458
0.56 IPS |
|
|
Pose Estimation (F16)
|
100% |
461
0.56 IPS |
|
|
Pose Estimation (I8)
|
100% |
2051
2.48 IPS |
|
|
Object Detection (F32)
|
100% |
242
18.0 IPS |
|
|
Object Detection (F16)
|
100% |
240
17.9 IPS |
|
|
Object Detection (I8)
|
61% |
490
36.6 IPS |
|
|
Face Detection (F32)
|
100% |
472
5.61 IPS |
|
|
Face Detection (F16)
|
100% |
472
5.61 IPS |
|
|
Face Detection (I8)
|
86% |
1145
13.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
482
3.74 IPS |
|
|
Depth Estimation (F16)
|
100% |
484
3.75 IPS |
|
|
Depth Estimation (I8)
|
95% |
1142
8.85 IPS |
|
|
Style Transfer (F32)
|
100% |
832
1.09 IPS |
|
|
Style Transfer (F16)
|
100% |
827
1.09 IPS |
|
|
Style Transfer (I8)
|
98% |
2229
2.93 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
285
10.2 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
282
10.1 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
856
30.6 IPS |
|
|
Text Classification (F32)
|
100% |
294
422.3 IPS |
|
|
Text Classification (F16)
|
100% |
304
437.0 IPS |
|
|
Text Classification (I8)
|
92% |
245
352.4 IPS |
|
|
Machine Translation (F32)
|
100% |
547
10.1 IPS |
|
|
Machine Translation (F16)
|
100% |
540
9.93 IPS |
|
|
Machine Translation (I8)
|
64% |
308
5.66 IPS |
