HONOR BVL-AN16
Geekbench ML Score
450
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 07:56 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% |
207
38.7 IPS |
|
|
Image Classification (F16)
|
100% |
206
38.6 IPS |
|
|
Image Classification (I8)
|
97% |
527
98.6 IPS |
|
|
Image Segmentation (F32)
|
100% |
259
4.32 IPS |
|
|
Image Segmentation (F16)
|
100% |
257
4.30 IPS |
|
|
Image Segmentation (I8)
|
98% |
524
8.75 IPS |
|
|
Pose Estimation (F32)
|
100% |
353
0.43 IPS |
|
|
Pose Estimation (F16)
|
100% |
353
0.43 IPS |
|
|
Pose Estimation (I8)
|
100% |
1357
1.64 IPS |
|
|
Object Detection (F32)
|
100% |
207
15.5 IPS |
|
|
Object Detection (F16)
|
100% |
205
15.3 IPS |
|
|
Object Detection (I8)
|
61% |
539
40.3 IPS |
|
|
Face Detection (F32)
|
100% |
511
6.07 IPS |
|
|
Face Detection (F16)
|
100% |
511
6.08 IPS |
|
|
Face Detection (I8)
|
86% |
1155
13.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
450
3.49 IPS |
|
|
Depth Estimation (F16)
|
100% |
450
3.49 IPS |
|
|
Depth Estimation (I8)
|
95% |
1202
9.32 IPS |
|
|
Style Transfer (F32)
|
100% |
728
0.96 IPS |
|
|
Style Transfer (F16)
|
100% |
729
0.96 IPS |
|
|
Style Transfer (I8)
|
98% |
1708
2.25 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
241
8.60 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
242
8.64 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
817
29.2 IPS |
|
|
Text Classification (F32)
|
100% |
270
387.6 IPS |
|
|
Text Classification (F16)
|
100% |
270
388.0 IPS |
|
|
Text Classification (I8)
|
92% |
447
643.0 IPS |
|
|
Machine Translation (F32)
|
100% |
496
9.12 IPS |
|
|
Machine Translation (F16)
|
100% |
499
9.18 IPS |
|
|
Machine Translation (I8)
|
64% |
539
9.91 IPS |
