iPad (9th generation)
Geekbench ML Score
1706
Core ML Neural Engine Inference Score
Geekbench ML 0.6.0 for iOS AArch64
Result Information
| Upload Date | May 18 2024 09:22 AM |
| Views | 3 |
System Information
| System Information | |
|---|---|
| Operating System | iOS 17.5 |
| Model | iPad (9th generation) |
| Model ID | iPad12,1 |
| Motherboard | J181AP |
| CPU Information | |
|---|---|
| Name | Apple A13 Bionic |
| Topology | 1 Processor, 6 Cores |
| Identifier | ARM |
| Base Frequency | 2.66 GHz |
| Cluster 1 | 2 Cores |
| Cluster 2 | 4 Cores |
| L1 Instruction Cache | 96.0 KB x 1 |
| L1 Data Cache | 48.0 KB x 1 |
| L2 Cache | 4.00 MB x 1 |
| Memory Information | |
|---|---|
| Size | 2.87 GB |
| Inference Information | |
|---|---|
| Framework | Core ML |
| Backend | Neural Engine |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
278
52.0 IPS |
|
|
Image Classification (F16)
|
100% |
2675
500.6 IPS |
|
|
Image Classification (I8)
|
99% |
2637
493.5 IPS |
|
|
Image Segmentation (F32)
|
100% |
219
3.66 IPS |
|
|
Image Segmentation (F16)
|
100% |
331
5.53 IPS |
|
|
Image Segmentation (I8)
|
100% |
331
5.53 IPS |
|
|
Pose Estimation (F32)
|
100% |
2259
2.74 IPS |
|
|
Pose Estimation (F16)
|
100% |
25695
31.1 IPS |
|
|
Pose Estimation (I8)
|
100% |
25622
31.0 IPS |
|
|
Object Detection (F32)
|
100% |
402
30.0 IPS |
|
|
Object Detection (F16)
|
100% |
1095
81.8 IPS |
|
|
Object Detection (I8)
|
97% |
497
37.1 IPS |
|
|
Face Detection (F32)
|
100% |
2380
28.3 IPS |
|
|
Face Detection (F16)
|
98% |
4890
58.1 IPS |
|
|
Face Detection (I8)
|
97% |
5124
60.9 IPS |
|
|
Depth Estimation (F32)
|
100% |
2472
19.2 IPS |
|
|
Depth Estimation (F16)
|
100% |
20586
159.6 IPS |
|
|
Depth Estimation (I8)
|
99% |
21597
167.5 IPS |
|
|
Style Transfer (F32)
|
100% |
5887
7.74 IPS |
|
|
Style Transfer (F16)
|
100% |
18071
23.8 IPS |
|
|
Style Transfer (I8)
|
100% |
18298
24.1 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
1036
37.0 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
4803
171.5 IPS |
|
|
Image Super-Resolution (I8)
|
100% |
5082
181.5 IPS |
|
|
Text Classification (F32)
|
100% |
231
331.7 IPS |
|
|
Text Classification (F16)
|
100% |
295
424.1 IPS |
|
|
Text Classification (I8)
|
96% |
296
425.9 IPS |
|
|
Machine Translation (F32)
|
100% |
208
3.82 IPS |
|
|
Machine Translation (F16)
|
100% |
203
3.74 IPS |
|
|
Machine Translation (I8)
|
99% |
218
4.01 IPS |
