alps k6878v1_64_ssdev
Geekbench ML Score
1522
TensorFlow Lite NNAPI Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | September 04 2025 07:39 AM |
| Views | 9 |
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% |
1521
284.7 IPS |
|
|
Image Classification (I8)
|
97% |
4570
855.2 IPS |
|
|
Image Segmentation (F32)
|
100% |
547
9.13 IPS |
|
|
Image Segmentation (F16)
|
100% |
1584
26.5 IPS |
|
|
Image Segmentation (I8)
|
98% |
1995
33.3 IPS |
|
|
Pose Estimation (F32)
|
100% |
2590
3.14 IPS |
|
|
Pose Estimation (F16)
|
100% |
5200
6.30 IPS |
|
|
Pose Estimation (I8)
|
100% |
26092
31.6 IPS |
|
|
Object Detection (F32)
|
100% |
313
23.4 IPS |
|
|
Object Detection (F16)
|
100% |
949
70.9 IPS |
|
|
Object Detection (I8)
|
64% |
1795
134.0 IPS |
|
|
Face Detection (F32)
|
100% |
1312
15.6 IPS |
|
|
Face Detection (F16)
|
97% |
3783
45.0 IPS |
|
|
Face Detection (I8)
|
86% |
8744
104.0 IPS |
|
|
Depth Estimation (F32)
|
100% |
401
3.11 IPS |
|
|
Depth Estimation (F16)
|
100% |
6934
53.8 IPS |
|
|
Depth Estimation (I8)
|
95% |
14474
112.2 IPS |
|
|
Style Transfer (F32)
|
100% |
1642
2.16 IPS |
|
|
Style Transfer (F16)
|
100% |
1446
1.90 IPS |
|
|
Style Transfer (I8)
|
31% |
4380
5.76 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
966
34.5 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
4261
152.2 IPS |
|
|
Image Super-Resolution (I8)
|
33% |
10495
374.8 IPS |
|
|
Text Classification (F32)
|
100% |
68
97.8 IPS |
|
|
Text Classification (F16)
|
100% |
637
915.4 IPS |
|
|
Text Classification (I8)
|
92% |
870
1.25 KIPS |
|
|
Machine Translation (F32)
|
100% |
185
3.41 IPS |
|
|
Machine Translation (F16)
|
100% |
181
3.34 IPS |
|
|
Machine Translation (I8)
|
66% |
315
5.79 IPS |
