samsung SM-A166P
Geekbench ML Score
202
TensorFlow Lite CPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | July 25 2025 09:16 PM |
| Views | 6 |
System Information
| System Information | |
|---|---|
| Operating System | Android 15 |
| Model | samsung SM-A166P |
| Model ID | samsung SM-A166P |
| Motherboard | a16xm |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 4 part 3339 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.40 GHz |
| Memory Information | |
|---|---|
| Size | 3.35 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
141
26.4 IPS |
|
|
Image Classification (F16)
|
100% |
74
13.8 IPS |
|
|
Image Classification (I8)
|
97% |
94
17.6 IPS |
|
|
Image Segmentation (F32)
|
100% |
211
3.53 IPS |
|
|
Image Segmentation (F16)
|
100% |
187
3.12 IPS |
|
|
Image Segmentation (I8)
|
98% |
258
4.30 IPS |
|
|
Pose Estimation (F32)
|
100% |
347
0.42 IPS |
|
|
Pose Estimation (F16)
|
100% |
346
0.42 IPS |
|
|
Pose Estimation (I8)
|
100% |
1294
1.57 IPS |
|
|
Object Detection (F32)
|
100% |
159
11.9 IPS |
|
|
Object Detection (F16)
|
100% |
96
7.19 IPS |
|
|
Object Detection (I8)
|
61% |
149
11.1 IPS |
|
|
Face Detection (F32)
|
100% |
255
3.03 IPS |
|
|
Face Detection (F16)
|
100% |
279
3.31 IPS |
|
|
Face Detection (I8)
|
86% |
414
4.93 IPS |
|
|
Depth Estimation (F32)
|
100% |
258
2.00 IPS |
|
|
Depth Estimation (F16)
|
100% |
265
2.05 IPS |
|
|
Depth Estimation (I8)
|
95% |
483
3.75 IPS |
|
|
Style Transfer (F32)
|
100% |
519
0.68 IPS |
|
|
Style Transfer (F16)
|
100% |
489
0.64 IPS |
|
|
Style Transfer (I8)
|
98% |
1641
2.16 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
137
4.90 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
137
4.88 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
363
13.0 IPS |
|
|
Text Classification (F32)
|
100% |
51
72.6 IPS |
|
|
Text Classification (F16)
|
100% |
65
93.9 IPS |
|
|
Text Classification (I8)
|
92% |
53
76.1 IPS |
|
|
Machine Translation (F32)
|
100% |
99
1.82 IPS |
|
|
Machine Translation (F16)
|
100% |
84
1.55 IPS |
|
|
Machine Translation (I8)
|
64% |
107
1.97 IPS |
