samsung SM-A166W
Geekbench AI Score
556
Single Precision Score
1000
Half Precision Score
1172
Quantized Score
Geekbench AI 1.7.0 for Android AArch64
Result Information
| User | M91 |
| Upload Date | April 05 2026 09:49 AM |
| Views | 3 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | ARM ARMv8 |
| System Information | |
|---|---|
| Operating System | Android 16 |
| Model | samsung SM-A166W |
| Model ID | samsung SM-A166W |
| Motherboard | s5e8535 |
| Governor | energy_aware |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 1 part 3393 revision 1 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.40 GHz |
| Memory Information | |
|---|---|
| Size | 3.37 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
237
44.0 IPS |
|
|
Image Classification (HP)
|
100% |
502
93.3 IPS |
|
|
Image Classification (Q)
|
99% |
777
145.0 IPS |
|
|
Image Segmentation (SP)
|
100% |
323
5.24 IPS |
|
|
Image Segmentation (HP)
|
100% |
598
9.69 IPS |
|
|
Image Segmentation (Q)
|
98% |
888
14.4 IPS |
|
|
Pose Estimation (SP)
|
100% |
1189
1.39 IPS |
|
|
Pose Estimation (HP)
|
92% |
2403
2.82 IPS |
|
|
Pose Estimation (Q)
|
96% |
1833
2.15 IPS |
|
|
Object Detection (SP)
|
100% |
234
18.5 IPS |
|
|
Object Detection (HP)
|
97% |
469
37.3 IPS |
|
|
Object Detection (Q)
|
85% |
757
61.0 IPS |
|
|
Face Detection (SP)
|
100% |
794
9.44 IPS |
|
|
Face Detection (HP)
|
100% |
1445
17.2 IPS |
|
|
Face Detection (Q)
|
97% |
1796
21.4 IPS |
|
|
Depth Estimation (SP)
|
100% |
1052
8.11 IPS |
|
|
Depth Estimation (HP)
|
98% |
1971
15.2 IPS |
|
|
Depth Estimation (Q)
|
64% |
1696
15.6 IPS |
|
|
Style Transfer (SP)
|
89% |
1992
2.59 IPS |
|
|
Style Transfer (HP)
|
88% |
2858
3.71 IPS |
|
|
Style Transfer (Q)
|
98% |
4259
5.49 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
572
21.1 IPS |
|
|
Image Super-Resolution (HP)
|
92% |
1034
38.4 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
1075
39.8 IPS |
|
|
Text Classification (SP)
|
100% |
298
397.8 IPS |
|
|
Text Classification (HP)
|
100% |
508
678.5 IPS |
|
|
Text Classification (Q)
|
93% |
667
895.6 IPS |
|
|
Machine Translation (SP)
|
100% |
474
8.16 IPS |
|
|
Machine Translation (HP)
|
96% |
694
12.0 IPS |
|
|
Machine Translation (Q)
|
55% |
552
14.8 IPS |
