alps k6878v1_64_ssdev
Geekbench ML Score
1516
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 04 2025 05:12 AM |
| Views | 1 |
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% |
428
80.1 IPS |
|
|
Image Classification (F16)
|
100% |
1533
286.9 IPS |
|
|
Image Classification (I8)
|
97% |
4582
857.4 IPS |
|
|
Image Segmentation (F32)
|
100% |
548
9.15 IPS |
|
|
Image Segmentation (F16)
|
100% |
1573
26.3 IPS |
|
|
Image Segmentation (I8)
|
98% |
1996
33.3 IPS |
|
|
Pose Estimation (F32)
|
100% |
2595
3.14 IPS |
|
|
Pose Estimation (F16)
|
100% |
5290
6.41 IPS |
|
|
Pose Estimation (I8)
|
100% |
26150
31.7 IPS |
|
|
Object Detection (F32)
|
100% |
314
23.4 IPS |
|
|
Object Detection (F16)
|
100% |
969
72.3 IPS |
|
|
Object Detection (I8)
|
64% |
1797
134.1 IPS |
|
|
Face Detection (F32)
|
100% |
1310
15.6 IPS |
|
|
Face Detection (F16)
|
97% |
3790
45.1 IPS |
|
|
Face Detection (I8)
|
86% |
8844
105.2 IPS |
|
|
Depth Estimation (F32)
|
100% |
401
3.11 IPS |
|
|
Depth Estimation (F16)
|
100% |
6951
53.9 IPS |
|
|
Depth Estimation (I8)
|
95% |
14100
109.3 IPS |
|
|
Style Transfer (F32)
|
100% |
1644
2.16 IPS |
|
|
Style Transfer (F16)
|
100% |
1449
1.91 IPS |
|
|
Style Transfer (I8)
|
31% |
4392
5.78 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
967
34.5 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
4279
152.8 IPS |
|
|
Image Super-Resolution (I8)
|
33% |
10386
370.9 IPS |
|
|
Text Classification (F32)
|
100% |
69
98.6 IPS |
|
|
Text Classification (F16)
|
100% |
600
862.1 IPS |
|
|
Text Classification (I8)
|
92% |
775
1.11 KIPS |
|
|
Machine Translation (F32)
|
100% |
185
3.40 IPS |
|
|
Machine Translation (F16)
|
100% |
181
3.33 IPS |
|
|
Machine Translation (I8)
|
66% |
316
5.81 IPS |
