alps k6878v1_64_ssdev
Geekbench ML Score
1514
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 03 2025 04:19 AM |
| Views | 2 |
System Information
| System Information | |
|---|---|
| Operating System | Android 14 |
| Model | alps k6878v1_64_ssdev |
| Model ID | alps k6878v1_64_ssdev |
| Motherboard | k6878v1_64 |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 4 Cores @ 2.00 GHz |
| Cluster 2 | 4 Cores @ 2.50 GHz |
| Memory Information | |
|---|---|
| Size | 7.20 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | NNAPI |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
425
79.5 IPS |
|
|
Image Classification (F16)
|
100% |
1514
283.4 IPS |
|
|
Image Classification (I8)
|
97% |
4533
848.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
545
9.10 IPS |
|
|
Image Segmentation (F16)
|
100% |
1557
26.0 IPS |
|
|
Image Segmentation (I8)
|
98% |
1976
33.0 IPS |
|
|
Pose Estimation (F32)
|
100% |
2648
3.21 IPS |
|
|
Pose Estimation (F16)
|
100% |
5547
6.72 IPS |
|
|
Pose Estimation (I8)
|
100% |
26087
31.6 IPS |
|
|
Object Detection (F32)
|
100% |
315
23.5 IPS |
|
|
Object Detection (F16)
|
100% |
902
67.4 IPS |
|
|
Object Detection (I8)
|
64% |
1773
132.3 IPS |
|
|
Face Detection (F32)
|
100% |
1310
15.6 IPS |
|
|
Face Detection (F16)
|
97% |
3738
44.4 IPS |
|
|
Face Detection (I8)
|
86% |
8732
103.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
401
3.11 IPS |
|
|
Depth Estimation (F16)
|
100% |
6930
53.7 IPS |
|
|
Depth Estimation (I8)
|
95% |
14122
109.5 IPS |
|
|
Style Transfer (F32)
|
100% |
1643
2.16 IPS |
|
|
Style Transfer (F16)
|
100% |
1469
1.93 IPS |
|
|
Style Transfer (I8)
|
31% |
4388
5.77 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
969
34.6 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
3965
141.6 IPS |
|
|
Image Super-Resolution (I8)
|
33% |
10804
385.8 IPS |
|
|
Text Classification (F32)
|
100% |
68
98.2 IPS |
|
|
Text Classification (F16)
|
100% |
634
911.4 IPS |
|
|
Text Classification (I8)
|
92% |
820
1.18 KIPS |
|
|
Machine Translation (F32)
|
100% |
184
3.39 IPS |
|
|
Machine Translation (F16)
|
100% |
180
3.30 IPS |
|
|
Machine Translation (I8)
|
66% |
312
5.75 IPS |
