HONOR ELP-NX9
Geekbench ML Score
588
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 04 2024 07:50 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | HONOR ELP-NX9 |
| Model ID | HONOR ELP-NX9 |
| Motherboard | ELP |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3458 revision 1 |
| Base Frequency | 2.02 GHz |
| Cluster 1 | 3 Cores @ 2.02 GHz |
| Cluster 2 | 4 Cores @ 2.80 GHz |
| Cluster 3 | 1 Core @ 3.01 GHz |
| Memory Information | |
|---|---|
| Size | 11.04 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
274
51.2 IPS |
|
|
Image Classification (F16)
|
100% |
266
49.8 IPS |
|
|
Image Classification (I8)
|
97% |
690
129.1 IPS |
|
|
Image Segmentation (F32)
|
100% |
327
5.47 IPS |
|
|
Image Segmentation (F16)
|
100% |
328
5.48 IPS |
|
|
Image Segmentation (I8)
|
98% |
686
11.5 IPS |
|
|
Pose Estimation (F32)
|
100% |
450
0.55 IPS |
|
|
Pose Estimation (F16)
|
100% |
450
0.54 IPS |
|
|
Pose Estimation (I8)
|
100% |
1785
2.16 IPS |
|
|
Object Detection (F32)
|
100% |
280
20.9 IPS |
|
|
Object Detection (F16)
|
100% |
273
20.4 IPS |
|
|
Object Detection (I8)
|
61% |
761
56.8 IPS |
|
|
Face Detection (F32)
|
100% |
637
7.57 IPS |
|
|
Face Detection (F16)
|
100% |
633
7.53 IPS |
|
|
Face Detection (I8)
|
86% |
1480
17.6 IPS |
|
|
Depth Estimation (F32)
|
100% |
552
4.28 IPS |
|
|
Depth Estimation (F16)
|
100% |
552
4.28 IPS |
|
|
Depth Estimation (I8)
|
95% |
1554
12.1 IPS |
|
|
Style Transfer (F32)
|
100% |
918
1.21 IPS |
|
|
Style Transfer (F16)
|
100% |
916
1.21 IPS |
|
|
Style Transfer (I8)
|
98% |
2221
2.92 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
299
10.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
303
10.8 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
1110
39.7 IPS |
|
|
Text Classification (F32)
|
100% |
392
563.2 IPS |
|
|
Text Classification (F16)
|
100% |
397
569.9 IPS |
|
|
Text Classification (I8)
|
92% |
654
940.0 IPS |
|
|
Machine Translation (F32)
|
100% |
644
11.9 IPS |
|
|
Machine Translation (F16)
|
100% |
628
11.6 IPS |
|
|
Machine Translation (I8)
|
64% |
735
13.5 IPS |
