HONOR BVL-AN16
Geekbench ML Score
288
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 25 2024 08:21 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 | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
595
111.3 IPS |
|
|
Image Classification (F16)
|
100% |
486
90.9 IPS |
|
|
Image Classification (I8)
|
97% |
834
156.1 IPS |
|
|
Image Segmentation (F32)
|
100% |
641
10.7 IPS |
|
|
Image Segmentation (F16)
|
100% |
633
10.6 IPS |
|
|
Image Segmentation (I8)
|
98% |
589
9.83 IPS |
|
|
Pose Estimation (F32)
|
100% |
896
1.09 IPS |
|
|
Pose Estimation (F16)
|
100% |
921
1.12 IPS |
|
|
Pose Estimation (I8)
|
100% |
2662
3.22 IPS |
|
|
Object Detection (F32)
|
100% |
117
8.72 IPS |
|
|
Object Detection (F16)
|
100% |
118
8.78 IPS |
|
|
Object Detection (I8)
|
61% |
136
10.1 IPS |
|
|
Face Detection (F32)
|
100% |
401
4.77 IPS |
|
|
Face Detection (F16)
|
100% |
402
4.77 IPS |
|
|
Face Detection (I8)
|
86% |
451
5.36 IPS |
|
|
Depth Estimation (F32)
|
100% |
416
3.23 IPS |
|
|
Depth Estimation (F16)
|
100% |
417
3.23 IPS |
|
|
Depth Estimation (I8)
|
95% |
487
3.78 IPS |
|
|
Style Transfer (F32)
|
100% |
1849
2.43 IPS |
|
|
Style Transfer (F16)
|
100% |
1836
2.42 IPS |
|
|
Style Transfer (I8)
|
98% |
3705
4.87 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
246
8.78 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
240
8.58 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
302
10.8 IPS |
|
|
Text Classification (F32)
|
100% |
15
21.2 IPS |
|
|
Text Classification (F16)
|
100% |
15
21.3 IPS |
|
|
Text Classification (I8)
|
92% |
16
23.3 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 |
