HONOR BVL-AN16
Geekbench ML Score
308
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 06:57 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | HONOR BVL-AN16 |
| Model ID | HONOR BVL-AN16 |
| Motherboard | BVL |
| Governor | walt |
| CPU Information | |
|---|---|
| Name | ARM SM8650 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3458 revision 1 |
| Base Frequency | 2.26 GHz |
| Cluster 1 | 2 Cores @ 2.27 GHz |
| Cluster 2 | 2 Cores @ 2.96 GHz |
| Cluster 3 | 3 Cores @ 3.15 GHz |
| Cluster 4 | 1 Core @ 3.30 GHz |
| Memory Information | |
|---|---|
| Size | 11.00 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
201
37.5 IPS |
|
|
Image Classification (F16)
|
100% |
200
37.5 IPS |
|
|
Image Classification (I8)
|
97% |
506
94.8 IPS |
|
|
Image Segmentation (F32)
|
100% |
249
4.16 IPS |
|
|
Image Segmentation (F16)
|
100% |
166
2.77 IPS |
|
|
Image Segmentation (I8)
|
98% |
331
5.52 IPS |
|
|
Pose Estimation (F32)
|
100% |
213
0.26 IPS |
|
|
Pose Estimation (F16)
|
100% |
212
0.26 IPS |
|
|
Pose Estimation (I8)
|
100% |
805
0.97 IPS |
|
|
Object Detection (F32)
|
100% |
130
9.72 IPS |
|
|
Object Detection (F16)
|
100% |
130
9.73 IPS |
|
|
Object Detection (I8)
|
61% |
347
25.9 IPS |
|
|
Face Detection (F32)
|
100% |
382
4.54 IPS |
|
|
Face Detection (F16)
|
100% |
382
4.54 IPS |
|
|
Face Detection (I8)
|
86% |
730
8.68 IPS |
|
|
Depth Estimation (F32)
|
100% |
299
2.32 IPS |
|
|
Depth Estimation (F16)
|
100% |
300
2.33 IPS |
|
|
Depth Estimation (I8)
|
95% |
792
6.14 IPS |
|
|
Style Transfer (F32)
|
100% |
532
0.70 IPS |
|
|
Style Transfer (F16)
|
100% |
529
0.70 IPS |
|
|
Style Transfer (I8)
|
98% |
1460
1.92 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
147
5.24 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
147
5.25 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
501
17.9 IPS |
|
|
Text Classification (F32)
|
100% |
161
231.8 IPS |
|
|
Text Classification (F16)
|
100% |
161
231.8 IPS |
|
|
Text Classification (I8)
|
92% |
264
380.0 IPS |
|
|
Machine Translation (F32)
|
100% |
315
5.81 IPS |
|
|
Machine Translation (F16)
|
100% |
316
5.81 IPS |
|
|
Machine Translation (I8)
|
64% |
355
6.53 IPS |
