HONOR BVL-AN16
Geekbench ML Score
1145
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 06:18 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 | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
829
155.1 IPS |
|
|
Image Classification (F16)
|
100% |
1270
237.7 IPS |
|
|
Image Classification (I8)
|
99% |
1172
219.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
820
13.7 IPS |
|
|
Image Segmentation (F16)
|
100% |
1301
21.7 IPS |
|
|
Image Segmentation (I8)
|
98% |
1018
17.0 IPS |
|
|
Pose Estimation (F32)
|
100% |
1060
1.28 IPS |
|
|
Pose Estimation (F16)
|
100% |
2476
3.00 IPS |
|
|
Pose Estimation (I8)
|
100% |
2465
2.98 IPS |
|
|
Object Detection (F32)
|
100% |
236
17.6 IPS |
|
|
Object Detection (F16)
|
100% |
393
29.3 IPS |
|
|
Object Detection (I8)
|
62% |
245
18.3 IPS |
|
|
Face Detection (F32)
|
100% |
2590
30.8 IPS |
|
|
Face Detection (F16)
|
97% |
4308
51.2 IPS |
|
|
Face Detection (I8)
|
86% |
2988
35.5 IPS |
|
|
Depth Estimation (F32)
|
100% |
1822
14.1 IPS |
|
|
Depth Estimation (F16)
|
100% |
3700
28.7 IPS |
|
|
Depth Estimation (I8)
|
95% |
3677
28.5 IPS |
|
|
Style Transfer (F32)
|
100% |
5250
6.91 IPS |
|
|
Style Transfer (F16)
|
100% |
7766
10.2 IPS |
|
|
Style Transfer (I8)
|
98% |
7844
10.3 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
417
14.9 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
723
25.8 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
717
25.6 IPS |
|
|
Text Classification (F32)
|
100% |
279
400.7 IPS |
|
|
Text Classification (F16)
|
100% |
279
400.8 IPS |
|
|
Text Classification (I8)
|
92% |
465
667.7 IPS |
|
|
Machine Translation (F32)
|
100% |
509
9.37 IPS |
|
|
Machine Translation (F16)
|
100% |
505
9.28 IPS |
|
|
Machine Translation (I8)
|
64% |
555
10.2 IPS |
