HONOR BVL-AN16
Geekbench ML Score
314
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 26 2024 07:51 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% |
208
39.0 IPS |
|
|
Image Classification (F16)
|
100% |
222
41.6 IPS |
|
|
Image Classification (I8)
|
97% |
524
98.0 IPS |
|
|
Image Segmentation (F32)
|
100% |
258
4.31 IPS |
|
|
Image Segmentation (F16)
|
100% |
230
3.85 IPS |
|
|
Image Segmentation (I8)
|
98% |
330
5.51 IPS |
|
|
Pose Estimation (F32)
|
100% |
212
0.26 IPS |
|
|
Pose Estimation (F16)
|
100% |
213
0.26 IPS |
|
|
Pose Estimation (I8)
|
100% |
800
0.97 IPS |
|
|
Object Detection (F32)
|
100% |
130
9.74 IPS |
|
|
Object Detection (F16)
|
100% |
130
9.69 IPS |
|
|
Object Detection (I8)
|
61% |
347
25.9 IPS |
|
|
Face Detection (F32)
|
100% |
379
4.51 IPS |
|
|
Face Detection (F16)
|
100% |
380
4.52 IPS |
|
|
Face Detection (I8)
|
86% |
729
8.67 IPS |
|
|
Depth Estimation (F32)
|
100% |
300
2.32 IPS |
|
|
Depth Estimation (F16)
|
100% |
299
2.32 IPS |
|
|
Depth Estimation (I8)
|
95% |
793
6.15 IPS |
|
|
Style Transfer (F32)
|
100% |
531
0.70 IPS |
|
|
Style Transfer (F16)
|
100% |
530
0.70 IPS |
|
|
Style Transfer (I8)
|
98% |
1459
1.92 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
147
5.25 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
147
5.24 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
502
17.9 IPS |
|
|
Text Classification (F32)
|
100% |
161
231.8 IPS |
|
|
Text Classification (F16)
|
100% |
162
232.5 IPS |
|
|
Text Classification (I8)
|
92% |
267
383.1 IPS |
|
|
Machine Translation (F32)
|
100% |
315
5.80 IPS |
|
|
Machine Translation (F16)
|
100% |
315
5.80 IPS |
|
|
Machine Translation (I8)
|
64% |
356
6.55 IPS |
