iPhone 12 Mini
Geekbench ML Score
3870
Core ML Neural Engine Inference Score
Geekbench ML 0.6.0 for iOS AArch64
Result Information
| Upload Date | May 01 2024 08:39 AM |
| Views | 1 |
System Information
| System Information | |
|---|---|
| Operating System | iOS 17.4.1 |
| Model | iPhone 12 Mini |
| Model ID | iPhone13,1 |
| Motherboard | D52gAP |
| CPU Information | |
|---|---|
| Name | Apple A14 Bionic |
| Topology | 1 Processor, 6 Cores |
| Identifier | ARM |
| Base Frequency | 2.99 GHz |
| Cluster 1 | 2 Cores |
| Cluster 2 | 4 Cores |
| L1 Instruction Cache | 128 KB x 1 |
| L1 Data Cache | 64.0 KB x 1 |
| L2 Cache | 4.00 MB x 1 |
| Memory Information | |
|---|---|
| Size | 3.57 GB |
| Inference Information | |
|---|---|
| Framework | Core ML |
| Backend | Neural Engine |
| Device | Default |
Inference Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (F32)
|
100% |
311
58.1 IPS |
|
|
Image Classification (F16)
|
100% |
4167
779.7 IPS |
|
|
Image Classification (I8)
|
99% |
4268
798.7 IPS |
|
|
Image Segmentation (F32)
|
100% |
548
9.15 IPS |
|
|
Image Segmentation (F16)
|
100% |
3124
52.2 IPS |
|
|
Image Segmentation (I8)
|
100% |
3135
52.4 IPS |
|
|
Pose Estimation (F32)
|
100% |
5697
6.90 IPS |
|
|
Pose Estimation (F16)
|
100% |
55834
67.6 IPS |
|
|
Pose Estimation (I8)
|
100% |
57849
70.1 IPS |
|
|
Object Detection (F32)
|
100% |
608
45.4 IPS |
|
|
Object Detection (F16)
|
100% |
3483
260.1 IPS |
|
|
Object Detection (I8)
|
97% |
590
44.0 IPS |
|
|
Face Detection (F32)
|
100% |
2758
32.8 IPS |
|
|
Face Detection (F16)
|
97% |
10264
122.0 IPS |
|
|
Face Detection (I8)
|
96% |
10246
121.8 IPS |
|
|
Depth Estimation (F32)
|
100% |
5366
41.6 IPS |
|
|
Depth Estimation (F16)
|
100% |
34245
265.6 IPS |
|
|
Depth Estimation (I8)
|
37% |
39297
304.7 IPS |
|
|
Style Transfer (F32)
|
100% |
11280
14.8 IPS |
|
|
Style Transfer (F16)
|
100% |
91908
120.9 IPS |
|
|
Style Transfer (I8)
|
100% |
105675
139.0 IPS |
|
|
Image Super-Resolution (F32)
|
100% |
1616
57.7 IPS |
|
|
Image Super-Resolution (F16)
|
100% |
15499
553.5 IPS |
|
|
Image Super-Resolution (I8)
|
100% |
14729
526.0 IPS |
|
|
Text Classification (F32)
|
100% |
248
357.0 IPS |
|
|
Text Classification (F16)
|
100% |
900
1.29 KIPS |
|
|
Text Classification (I8)
|
96% |
326
468.9 IPS |
|
|
Machine Translation (F32)
|
100% |
306
5.63 IPS |
|
|
Machine Translation (F16)
|
100% |
1012
18.6 IPS |
|
|
Machine Translation (I8)
|
99% |
331
6.10 IPS |
