alps k6878v1_64_ssdev
Geekbench ML Score
1518
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 04 2025 04:30 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% |
427
80.0 IPS |
|
|
Image Classification (F16)
|
100% |
1520
284.5 IPS |
|
|
Image Classification (I8)
|
97% |
4572
855.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
545
9.10 IPS |
|
|
Image Segmentation (F16)
|
100% |
1570
26.2 IPS |
|
|
Image Segmentation (I8)
|
98% |
1996
33.3 IPS |
|
|
Pose Estimation (F32)
|
100% |
2585
3.13 IPS |
|
|
Pose Estimation (F16)
|
100% |
5193
6.29 IPS |
|
|
Pose Estimation (I8)
|
100% |
26154
31.7 IPS |
|
|
Object Detection (F32)
|
100% |
313
23.4 IPS |
|
|
Object Detection (F16)
|
100% |
992
74.1 IPS |
|
|
Object Detection (I8)
|
64% |
1780
132.9 IPS |
|
|
Face Detection (F32)
|
100% |
1310
15.6 IPS |
|
|
Face Detection (F16)
|
97% |
3744
44.5 IPS |
|
|
Face Detection (I8)
|
86% |
8854
105.3 IPS |
|
|
Depth Estimation (F32)
|
100% |
400
3.11 IPS |
|
|
Depth Estimation (F16)
|
100% |
6935
53.8 IPS |
|
|
Depth Estimation (I8)
|
95% |
14796
114.7 IPS |
|
|
Style Transfer (F32)
|
100% |
1645
2.16 IPS |
|
|
Style Transfer (F16)
|
100% |
1450
1.91 IPS |
|
|
Style Transfer (I8)
|
31% |
4391
5.78 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
968
34.6 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
3967
141.7 IPS |
|
|
Image Super-Resolution (I8)
|
33% |
10583
378.0 IPS |
|
|
Text Classification (F32)
|
100% |
69
98.7 IPS |
|
|
Text Classification (F16)
|
100% |
598
859.0 IPS |
|
|
Text Classification (I8)
|
92% |
854
1.23 KIPS |
|
|
Machine Translation (F32)
|
100% |
185
3.41 IPS |
|
|
Machine Translation (F16)
|
100% |
182
3.34 IPS |
|
|
Machine Translation (I8)
|
66% |
316
5.82 IPS |
