HONOR DNY-NX9
Geekbench AI Score
598
Single Precision Score
611
Half Precision Score
1117
Quantized Score
Geekbench AI 1.5.0 for Android AArch64
Result Information
| Upload Date | September 09 2025 02:16 AM |
| Views | 2 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | ARM ARMv8 |
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | HONOR DNY-NX9 |
| Model ID | HONOR DNY-NX9 |
| Motherboard | crow |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3405 revision 1 |
| Base Frequency | 1.80 GHz |
| Cluster 1 | 4 Cores @ 1.80 GHz |
| Cluster 2 | 3 Cores @ 2.40 GHz |
| Cluster 3 | 1 Core @ 2.63 GHz |
| Memory Information | |
|---|---|
| Size | 11.12 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
767
142.5 IPS |
|
|
Image Classification (HP)
|
100% |
770
143.2 IPS |
|
|
Image Classification (Q)
|
100% |
1929
358.8 IPS |
|
|
Image Segmentation (SP)
|
100% |
753
12.2 IPS |
|
|
Image Segmentation (HP)
|
100% |
744
12.1 IPS |
|
|
Image Segmentation (Q)
|
98% |
1945
31.6 IPS |
|
|
Pose Estimation (SP)
|
100% |
1940
2.26 IPS |
|
|
Pose Estimation (HP)
|
100% |
1904
2.22 IPS |
|
|
Pose Estimation (Q)
|
84% |
2013
2.39 IPS |
|
|
Object Detection (SP)
|
98% |
364
28.9 IPS |
|
|
Object Detection (HP)
|
98% |
273
21.7 IPS |
|
|
Object Detection (Q)
|
83% |
805
65.0 IPS |
|
|
Face Detection (SP)
|
100% |
542
6.45 IPS |
|
|
Face Detection (HP)
|
100% |
548
6.51 IPS |
|
|
Face Detection (Q)
|
95% |
1228
14.7 IPS |
|
|
Depth Estimation (SP)
|
99% |
689
5.32 IPS |
|
|
Depth Estimation (HP)
|
99% |
693
5.35 IPS |
|
|
Depth Estimation (Q)
|
64% |
1497
13.7 IPS |
|
|
Style Transfer (SP)
|
89% |
1642
2.13 IPS |
|
|
Style Transfer (HP)
|
89% |
2180
2.83 IPS |
|
|
Style Transfer (Q)
|
98% |
3815
4.92 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
365
13.5 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
468
17.3 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
994
36.8 IPS |
|
|
Text Classification (SP)
|
100% |
180
239.9 IPS |
|
|
Text Classification (HP)
|
99% |
178
237.4 IPS |
|
|
Text Classification (Q)
|
88% |
296
399.9 IPS |
|
|
Machine Translation (SP)
|
100% |
361
6.22 IPS |
|
|
Machine Translation (HP)
|
100% |
358
6.16 IPS |
|
|
Machine Translation (Q)
|
50% |
242
8.59 IPS |
