samsung SM-A215U
Geekbench AI Score
124
Single Precision Score
120
Half Precision Score
85
Quantized Score
Geekbench AI 1.6.1 for Android AArch64
Result Information
| Upload Date | January 07 2026 04:39 AM |
| Views | 9 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | ARM MT6765V/CA |
| System Information | |
|---|---|
| Operating System | Android 12 |
| Model | samsung SM-A215U |
| Model ID | samsung SM-A215U |
| Motherboard | a21 |
| CPU Information | |
|---|---|
| Name | ARM MT6765V/CA |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3331 revision 4 |
| Base Frequency | 2.30 GHz |
| Cluster 1 | 4 Cores @ 1.80 GHz |
| Cluster 2 | 4 Cores @ 2.30 GHz |
| Memory Information | |
|---|---|
| Size | 2.69 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
76
14.1 IPS |
|
|
Image Classification (HP)
|
100% |
78
14.5 IPS |
|
|
Image Classification (Q)
|
100% |
67
12.5 IPS |
|
|
Image Segmentation (SP)
|
100% |
100
1.63 IPS |
|
|
Image Segmentation (HP)
|
100% |
100
1.61 IPS |
|
|
Image Segmentation (Q)
|
98% |
85
1.38 IPS |
|
|
Pose Estimation (SP)
|
100% |
414
0.48 IPS |
|
|
Pose Estimation (HP)
|
100% |
416
0.49 IPS |
|
|
Pose Estimation (Q)
|
96% |
400
0.47 IPS |
|
|
Object Detection (SP)
|
100% |
72
5.74 IPS |
|
|
Object Detection (HP)
|
100% |
75
5.92 IPS |
|
|
Object Detection (Q)
|
85% |
67
5.43 IPS |
|
|
Face Detection (SP)
|
100% |
219
2.60 IPS |
|
|
Face Detection (HP)
|
100% |
222
2.63 IPS |
|
|
Face Detection (Q)
|
97% |
173
2.06 IPS |
|
|
Depth Estimation (SP)
|
100% |
274
2.11 IPS |
|
|
Depth Estimation (HP)
|
99% |
250
1.93 IPS |
|
|
Depth Estimation (Q)
|
64% |
203
1.86 IPS |
|
|
Style Transfer (SP)
|
100% |
762
0.98 IPS |
|
|
Style Transfer (HP)
|
100% |
759
0.98 IPS |
|
|
Style Transfer (Q)
|
98% |
713
0.92 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
159
5.87 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
162
5.98 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
138
5.10 IPS |
|
|
Text Classification (SP)
|
35% |
7
63.5 IPS |
|
|
Text Classification (HP)
|
35% |
6
62.7 IPS |
|
|
Text Classification (Q)
|
32% |
4
57.5 IPS |
|
|
Machine Translation (SP)
|
100% |
80
1.37 IPS |
|
|
Machine Translation (HP)
|
100% |
64
1.10 IPS |
|
|
Machine Translation (Q)
|
41% |
10
0.68 IPS |
