HONOR BVL-AN16
Geekbench ML Score
296
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 03:00 AM |
| Views | 3 |
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 | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
611
114.4 IPS |
|
|
Image Classification (F16)
|
100% |
493
92.2 IPS |
|
|
Image Classification (I8)
|
97% |
789
147.6 IPS |
|
|
Image Segmentation (F32)
|
100% |
637
10.6 IPS |
|
|
Image Segmentation (F16)
|
100% |
636
10.6 IPS |
|
|
Image Segmentation (I8)
|
98% |
621
10.4 IPS |
|
|
Pose Estimation (F32)
|
100% |
1294
1.57 IPS |
|
|
Pose Estimation (F16)
|
100% |
1307
1.58 IPS |
|
|
Pose Estimation (I8)
|
100% |
2667
3.23 IPS |
|
|
Object Detection (F32)
|
100% |
118
8.77 IPS |
|
|
Object Detection (F16)
|
100% |
117
8.76 IPS |
|
|
Object Detection (I8)
|
61% |
137
10.2 IPS |
|
|
Face Detection (F32)
|
100% |
403
4.79 IPS |
|
|
Face Detection (F16)
|
100% |
404
4.81 IPS |
|
|
Face Detection (I8)
|
86% |
452
5.37 IPS |
|
|
Depth Estimation (F32)
|
100% |
418
3.24 IPS |
|
|
Depth Estimation (F16)
|
100% |
418
3.24 IPS |
|
|
Depth Estimation (I8)
|
95% |
486
3.77 IPS |
|
|
Style Transfer (F32)
|
100% |
1839
2.42 IPS |
|
|
Style Transfer (F16)
|
100% |
1858
2.44 IPS |
|
|
Style Transfer (I8)
|
98% |
3745
4.93 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
242
8.65 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
240
8.56 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
303
10.8 IPS |
|
|
Text Classification (F32)
|
100% |
15
21.2 IPS |
|
|
Text Classification (F16)
|
100% |
15
21.2 IPS |
|
|
Text Classification (I8)
|
92% |
16
23.2 IPS |
|
|
Machine Translation (F32)
|
100% |
37
0.67 IPS |
|
|
Machine Translation (F16)
|
100% |
37
0.67 IPS |
|
|
Machine Translation (I8)
|
64% |
39
0.72 IPS |
