Google nissa
Geekbench AI Score
15
Single Precision Score
15
Half Precision Score
8
Quantized Score
Geekbench AI 1.2.0 for Android AArch64
Result Information
| User | Hulkstern |
| Upload Date | March 11 2025 10:11 AM |
| Views | 5 |
System Information
| AI Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | CPU |
| Device | placeholder |
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | Google nissa |
| Model ID | Google nissa |
| Motherboard | yavijo |
| CPU Information | |
|---|---|
| Name | placeholder |
| Topology | 1 Processor, 4 Cores |
| Identifier | ARM implementer 78 architecture 8 variant 2 part 0 revision 1 |
| Base Frequency | 0 MHz |
| Cluster 1 | 4 Cores |
| Memory Information | |
|---|---|
| Size | 3.08 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
0% |
0
2.49 KIPS |
|
|
Image Classification (HP)
|
0% |
0
2.52 KIPS |
|
|
Image Classification (Q)
|
0% |
0
2.34 KIPS |
|
|
Image Segmentation (SP)
|
73% |
15112
261.1 IPS |
|
|
Image Segmentation (HP)
|
73% |
15394
266.0 IPS |
|
|
Image Segmentation (Q)
|
73% |
32412
560.1 IPS |
|
|
Pose Estimation (SP)
|
0% |
0
732.9 IPS |
|
|
Pose Estimation (HP)
|
0% |
0
712.5 IPS |
|
|
Pose Estimation (Q)
|
0% |
0
1.85 KIPS |
|
|
Object Detection (SP)
|
0% |
0
1.17 KIPS |
|
|
Object Detection (HP)
|
0% |
0
1.15 KIPS |
|
|
Object Detection (Q)
|
0% |
0
827.9 IPS |
|
|
Face Detection (SP)
|
0% |
0
333.4 IPS |
|
|
Face Detection (HP)
|
0% |
0
337.2 IPS |
|
|
Face Detection (Q)
|
0% |
0
554.4 IPS |
|
|
Depth Estimation (SP)
|
0% |
0
729.3 IPS |
|
|
Depth Estimation (HP)
|
0% |
0
733.0 IPS |
|
|
Depth Estimation (Q)
|
0% |
0
655.6 IPS |
|
|
Style Transfer (SP)
|
32% |
137387
1.72 KIPS |
|
|
Style Transfer (HP)
|
32% |
138360
1.74 KIPS |
|
|
Style Transfer (Q)
|
0% |
0
3.95 KIPS |
|
|
Image Super-Resolution (SP)
|
1% |
0
1.58 KIPS |
|
|
Image Super-Resolution (HP)
|
1% |
0
1.59 KIPS |
|
|
Image Super-Resolution (Q)
|
2% |
163
1.93 KIPS |
|
|
Text Classification (SP)
|
29% |
317
5.48 KIPS |
|
|
Text Classification (HP)
|
29% |
309
5.34 KIPS |
|
|
Text Classification (Q)
|
29% |
204
3.52 KIPS |
|
|
Machine Translation (SP)
|
0% |
0
6.92 IPS |
|
|
Machine Translation (HP)
|
0% |
0
6.70 IPS |
|
|
Machine Translation (Q)
|
0% |
0
4.95 IPS |
