HONOR REA-NX9
Geekbench AI Score
854
Single Precision Score
856
Half Precision Score
1962
Quantized Score
Geekbench AI 1.2.0 for Android AArch64
Result Information
| Upload Date | June 24 2025 03:51 PM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | ARM ARMv8 |
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | HONOR REA-NX9 |
| Model ID | HONOR REA-NX9 |
| Motherboard | taro |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 2 part 3399 revision 0 |
| Base Frequency | 1.80 GHz |
| Cluster 1 | 4 Cores @ 1.80 GHz |
| Cluster 2 | 3 Cores @ 2.36 GHz |
| Cluster 3 | 1 Core @ 2.52 GHz |
| Memory Information | |
|---|---|
| Size | 11.04 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
385
71.6 IPS |
|
|
Image Classification (HP)
|
100% |
414
76.9 IPS |
|
|
Image Classification (Q)
|
100% |
1476
274.5 IPS |
|
|
Image Segmentation (SP)
|
100% |
497
8.05 IPS |
|
|
Image Segmentation (HP)
|
100% |
498
8.07 IPS |
|
|
Image Segmentation (Q)
|
98% |
1653
26.9 IPS |
|
|
Pose Estimation (SP)
|
100% |
1921
2.24 IPS |
|
|
Pose Estimation (HP)
|
100% |
1916
2.24 IPS |
|
|
Pose Estimation (Q)
|
84% |
4555
5.40 IPS |
|
|
Object Detection (SP)
|
100% |
384
30.5 IPS |
|
|
Object Detection (HP)
|
100% |
384
30.4 IPS |
|
|
Object Detection (Q)
|
83% |
1433
115.8 IPS |
|
|
Face Detection (SP)
|
100% |
1077
12.8 IPS |
|
|
Face Detection (HP)
|
100% |
1093
13.0 IPS |
|
|
Face Detection (Q)
|
95% |
3291
39.3 IPS |
|
|
Depth Estimation (SP)
|
100% |
1674
12.9 IPS |
|
|
Depth Estimation (HP)
|
99% |
1634
12.6 IPS |
|
|
Depth Estimation (Q)
|
64% |
2470
22.6 IPS |
|
|
Style Transfer (SP)
|
89% |
3165
4.11 IPS |
|
|
Style Transfer (HP)
|
89% |
3220
4.18 IPS |
|
|
Style Transfer (Q)
|
98% |
9205
11.9 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
903
33.3 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
878
32.4 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
2265
83.9 IPS |
|
|
Text Classification (SP)
|
100% |
422
562.8 IPS |
|
|
Text Classification (HP)
|
100% |
415
554.3 IPS |
|
|
Text Classification (Q)
|
88% |
831
1.12 KIPS |
|
|
Machine Translation (SP)
|
100% |
675
11.6 IPS |
|
|
Machine Translation (HP)
|
100% |
667
11.5 IPS |
|
|
Machine Translation (Q)
|
50% |
378
13.4 IPS |
