HONOR NIC-LX2
Geekbench AI Score
73
Single Precision Score
72
Half Precision Score
128
Quantized Score
Geekbench AI 1.4.0 for Android AArch64
Result Information
| Upload Date | July 10 2025 09:32 AM |
| Views | 3 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | ARM MT6769V/CB |
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | HONOR NIC-LX2 |
| Model ID | HONOR NIC-LX2 |
| Motherboard | NICL |
| CPU Information | |
|---|---|
| Name | ARM MT6769V/CB |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 3 part 3338 revision 1 |
| Base Frequency | 1.70 GHz |
| Cluster 1 | 6 Cores @ 1.70 GHz |
| Cluster 2 | 2 Cores @ 2.00 GHz |
| Memory Information | |
|---|---|
| Size | 5.65 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
45
8.31 IPS |
|
|
Image Classification (HP)
|
100% |
45
8.37 IPS |
|
|
Image Classification (Q)
|
99% |
90
16.7 IPS |
|
|
Image Segmentation (SP)
|
100% |
62
1.01 IPS |
|
|
Image Segmentation (HP)
|
100% |
63
1.02 IPS |
|
|
Image Segmentation (Q)
|
98% |
109
1.78 IPS |
|
|
Pose Estimation (SP)
|
100% |
88
0.10 IPS |
|
|
Pose Estimation (HP)
|
100% |
77
0.09 IPS |
|
|
Pose Estimation (Q)
|
98% |
184
0.22 IPS |
|
|
Object Detection (SP)
|
100% |
46
3.62 IPS |
|
|
Object Detection (HP)
|
100% |
46
3.62 IPS |
|
|
Object Detection (Q)
|
87% |
80
6.44 IPS |
|
|
Face Detection (SP)
|
100% |
105
1.25 IPS |
|
|
Face Detection (HP)
|
100% |
106
1.26 IPS |
|
|
Face Detection (Q)
|
97% |
208
2.48 IPS |
|
|
Depth Estimation (SP)
|
100% |
94
0.72 IPS |
|
|
Depth Estimation (HP)
|
99% |
94
0.73 IPS |
|
|
Depth Estimation (Q)
|
64% |
163
1.48 IPS |
|
|
Style Transfer (SP)
|
100% |
159
0.20 IPS |
|
|
Style Transfer (HP)
|
100% |
157
0.20 IPS |
|
|
Style Transfer (Q)
|
98% |
368
0.47 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
46
1.71 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
46
1.71 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
104
3.87 IPS |
|
|
Text Classification (SP)
|
100% |
57
76.4 IPS |
|
|
Text Classification (HP)
|
100% |
57
76.4 IPS |
|
|
Text Classification (Q)
|
91% |
88
118.0 IPS |
|
|
Machine Translation (SP)
|
100% |
102
1.75 IPS |
|
|
Machine Translation (HP)
|
100% |
101
1.75 IPS |
|
|
Machine Translation (Q)
|
57% |
76
1.85 IPS |
