samsung SM-G715FN
Geekbench AI Score
239
Single Precision Score
240
Half Precision Score
300
Quantized Score
Geekbench AI 1.1.0 for Android AArch64
Result Information
| User | benched |
| Upload Date | October 22 2024 10:33 PM |
| Views | 15 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | ARM ARMv8 |
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | samsung SM-G715FN |
| Model ID | samsung SM-G715FN |
| Motherboard | exynos9611 |
| Governor | schedutil |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 0 part 3337 revision 2 |
| Base Frequency | 1.74 GHz |
| Cluster 1 | 4 Cores @ 1.74 GHz |
| Cluster 2 | 4 Cores @ 2.31 GHz |
| Memory Information | |
|---|---|
| Size | 3.49 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
111
20.7 IPS |
|
|
Image Classification (HP)
|
100% |
109
20.2 IPS |
|
|
Image Classification (Q)
|
100% |
201
37.3 IPS |
|
|
Image Segmentation (SP)
|
100% |
127
2.05 IPS |
|
|
Image Segmentation (HP)
|
100% |
128
2.07 IPS |
|
|
Image Segmentation (Q)
|
98% |
243
3.95 IPS |
|
|
Pose Estimation (SP)
|
100% |
452
0.53 IPS |
|
|
Pose Estimation (HP)
|
100% |
497
0.58 IPS |
|
|
Pose Estimation (Q)
|
84% |
412
0.49 IPS |
|
|
Object Detection (SP)
|
100% |
95
7.55 IPS |
|
|
Object Detection (HP)
|
100% |
97
7.67 IPS |
|
|
Object Detection (Q)
|
83% |
182
14.7 IPS |
|
|
Face Detection (SP)
|
100% |
339
4.02 IPS |
|
|
Face Detection (HP)
|
100% |
359
4.26 IPS |
|
|
Face Detection (Q)
|
95% |
508
6.06 IPS |
|
|
Depth Estimation (SP)
|
100% |
469
3.61 IPS |
|
|
Depth Estimation (HP)
|
99% |
454
3.50 IPS |
|
|
Depth Estimation (Q)
|
64% |
452
4.14 IPS |
|
|
Style Transfer (SP)
|
89% |
966
1.25 IPS |
|
|
Style Transfer (HP)
|
89% |
963
1.25 IPS |
|
|
Style Transfer (Q)
|
98% |
1252
1.61 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
259
9.56 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
254
9.39 IPS |
|
|
Image Super-Resolution (Q)
|
97% |
268
9.93 IPS |
|
|
Text Classification (SP)
|
100% |
128
170.8 IPS |
|
|
Text Classification (HP)
|
100% |
122
163.5 IPS |
|
|
Text Classification (Q)
|
88% |
184
248.8 IPS |
|
|
Machine Translation (SP)
|
100% |
205
3.53 IPS |
|
|
Machine Translation (HP)
|
100% |
202
3.48 IPS |
|
|
Machine Translation (Q)
|
50% |
114
4.04 IPS |
