alps k6878v1_64_ssdev
Geekbench ML Score
1509
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 03 2025 06:30 AM |
| Views | 3 |
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
80.2 IPS |
|
|
Image Classification (F16)
|
100% |
1518
284.1 IPS |
|
|
Image Classification (I8)
|
97% |
4527
847.0 IPS |
|
|
Image Segmentation (F32)
|
100% |
543
9.08 IPS |
|
|
Image Segmentation (F16)
|
100% |
1571
26.2 IPS |
|
|
Image Segmentation (I8)
|
98% |
1987
33.2 IPS |
|
|
Pose Estimation (F32)
|
100% |
2602
3.15 IPS |
|
|
Pose Estimation (F16)
|
100% |
5201
6.30 IPS |
|
|
Pose Estimation (I8)
|
100% |
26081
31.6 IPS |
|
|
Object Detection (F32)
|
100% |
312
23.3 IPS |
|
|
Object Detection (F16)
|
100% |
959
71.6 IPS |
|
|
Object Detection (I8)
|
64% |
1760
131.4 IPS |
|
|
Face Detection (F32)
|
100% |
1307
15.5 IPS |
|
|
Face Detection (F16)
|
97% |
3767
44.8 IPS |
|
|
Face Detection (I8)
|
86% |
8721
103.7 IPS |
|
|
Depth Estimation (F32)
|
100% |
400
3.10 IPS |
|
|
Depth Estimation (F16)
|
100% |
6938
53.8 IPS |
|
|
Depth Estimation (I8)
|
95% |
13436
104.2 IPS |
|
|
Style Transfer (F32)
|
100% |
1642
2.16 IPS |
|
|
Style Transfer (F16)
|
100% |
1459
1.92 IPS |
|
|
Style Transfer (I8)
|
31% |
4387
5.77 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
970
34.6 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
4245
151.6 IPS |
|
|
Image Super-Resolution (I8)
|
33% |
10514
375.5 IPS |
|
|
Text Classification (F32)
|
100% |
68
98.3 IPS |
|
|
Text Classification (F16)
|
100% |
580
833.0 IPS |
|
|
Text Classification (I8)
|
92% |
845
1.22 KIPS |
|
|
Machine Translation (F32)
|
100% |
184
3.39 IPS |
|
|
Machine Translation (F16)
|
100% |
179
3.30 IPS |
|
|
Machine Translation (I8)
|
66% |
313
5.76 IPS |
