Google corsola
Geekbench ML Score
92
TensorFlow Lite GPU Inference Score
Geekbench ML 0.6.0 for Android AArch64
Result Information
| Upload Date | August 15 2024 05:00 PM |
| Views | 3 |
System Information
| System Information | |
|---|---|
| Operating System | Android 13 |
| Model | Google corsola |
| Model ID | Google corsola |
| Motherboard | steelix |
| Governor | schedutil |
| CPU Information | |
|---|---|
| Name | ARM ARMv8 |
| Topology | 1 Processor, 8 Cores |
| Identifier | ARM implementer 65 architecture 8 variant 3 part 3339 revision 0 |
| Base Frequency | 2.00 GHz |
| Cluster 1 | 6 Cores @ 2.00 GHz |
| Cluster 2 | 2 Cores @ 2.05 GHz |
| Memory Information | |
|---|---|
| Size | 6.99 GB |
| Inference Information | |
|---|---|
| Framework | TensorFlow Lite |
| Backend | GPU |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
59
11.0 IPS |
|
|
Image Classification (F16)
|
100% |
55
10.3 IPS |
|
|
Image Classification (I8)
|
97% |
54
10.0 IPS |
|
|
Image Segmentation (F32)
|
100% |
78
1.31 IPS |
|
|
Image Segmentation (F16)
|
100% |
77
1.28 IPS |
|
|
Image Segmentation (I8)
|
98% |
77
1.28 IPS |
|
|
Pose Estimation (F32)
|
100% |
58
0.07 IPS |
|
|
Pose Estimation (F16)
|
100% |
42
0.05 IPS |
|
|
Pose Estimation (I8)
|
100% |
41
0.05 IPS |
|
|
Object Detection (F32)
|
100% |
52
3.85 IPS |
|
|
Object Detection (F16)
|
100% |
49
3.69 IPS |
|
|
Object Detection (I8)
|
56% |
50
3.71 IPS |
|
|
Face Detection (F32)
|
100% |
168
1.99 IPS |
|
|
Face Detection (F16)
|
98% |
168
2.00 IPS |
|
|
Face Detection (I8)
|
86% |
164
1.94 IPS |
|
|
Depth Estimation (F32)
|
100% |
108
0.84 IPS |
|
|
Depth Estimation (F16)
|
100% |
92
0.71 IPS |
|
|
Depth Estimation (I8)
|
94% |
92
0.71 IPS |
|
|
Style Transfer (F32)
|
100% |
242
0.32 IPS |
|
|
Style Transfer (F16)
|
100% |
198
0.26 IPS |
|
|
Style Transfer (I8)
|
98% |
199
0.26 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
68
2.41 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
59
2.11 IPS |
|
|
Image Super-Resolution (I8)
|
98% |
59
2.11 IPS |
|
|
Text Classification (F32)
|
100% |
84
121.0 IPS |
|
|
Text Classification (F16)
|
100% |
84
120.4 IPS |
|
|
Text Classification (I8)
|
92% |
190
273.5 IPS |
|
|
Machine Translation (F32)
|
100% |
161
2.96 IPS |
|
|
Machine Translation (F16)
|
100% |
162
2.97 IPS |
|
|
Machine Translation (I8)
|
64% |
240
4.41 IPS |
