HONOR DNY-NX9
Geekbench AI Score
345
Single Precision Score
544
Half Precision Score
495
Quantized Score
Geekbench AI 1.2.0 for Android AArch64
Result Information
| Upload Date | June 09 2025 08:21 AM |
| Views | 3 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| 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% |
318
59.1 IPS |
|
|
Image Classification (HP)
|
100% |
450
83.7 IPS |
|
|
Image Classification (Q)
|
96% |
349
65.2 IPS |
|
|
Image Segmentation (SP)
|
100% |
548
8.88 IPS |
|
|
Image Segmentation (HP)
|
100% |
876
14.2 IPS |
|
|
Image Segmentation (Q)
|
98% |
732
11.9 IPS |
|
|
Pose Estimation (SP)
|
100% |
341
0.40 IPS |
|
|
Pose Estimation (HP)
|
100% |
724
0.84 IPS |
|
|
Pose Estimation (Q)
|
96% |
724
0.85 IPS |
|
|
Object Detection (SP)
|
100% |
246
19.5 IPS |
|
|
Object Detection (HP)
|
100% |
360
28.5 IPS |
|
|
Object Detection (Q)
|
87% |
359
28.8 IPS |
|
|
Face Detection (SP)
|
100% |
1010
12.0 IPS |
|
|
Face Detection (HP)
|
100% |
1468
17.4 IPS |
|
|
Face Detection (Q)
|
97% |
1264
15.1 IPS |
|
|
Depth Estimation (SP)
|
100% |
674
5.19 IPS |
|
|
Depth Estimation (HP)
|
99% |
1310
10.1 IPS |
|
|
Depth Estimation (Q)
|
63% |
1071
9.93 IPS |
|
|
Style Transfer (SP)
|
100% |
1669
2.15 IPS |
|
|
Style Transfer (HP)
|
100% |
3680
4.73 IPS |
|
|
Style Transfer (Q)
|
98% |
3651
4.71 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
208
7.67 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
304
11.2 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
298
11.0 IPS |
|
|
Text Classification (SP)
|
100% |
63
83.8 IPS |
|
|
Text Classification (HP)
|
100% |
62
83.0 IPS |
|
|
Text Classification (Q)
|
91% |
88
118.2 IPS |
|
|
Machine Translation (SP)
|
100% |
112
1.94 IPS |
|
|
Machine Translation (HP)
|
100% |
167
2.87 IPS |
|
|
Machine Translation (Q)
|
57% |
104
2.54 IPS |
