iPhone 15
Geekbench AI Score
3499
Single Precision Score
23020
Half Precision Score
26411
Quantized Score
Geekbench AI 1.2.0 for iOS AArch64
Result Information
| User | justintea |
| Upload Date | December 03 2024 12:36 AM |
| Views | 4 |
System Information
| AI Information | |
|---|---|
| Framework | Core ML |
| Backend | Neural Engine |
| Device | Apple A16 Bionic |
| System Information | |
|---|---|
| Operating System | iOS 18.2 |
| Model | iPhone 15 |
| Model ID | iPhone15,4 |
| Motherboard | D37AP |
| CPU Information | |
|---|---|
| Name | Apple A16 Bionic |
| Topology | 1 Processor, 6 Cores |
| Identifier | ARM |
| Base Frequency | 3.46 GHz |
| Cluster 1 | 2 Cores |
| Cluster 2 | 4 Cores |
| Memory Information | |
|---|---|
| Size | 5.56 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
1411
262.4 IPS |
|
|
Image Classification (HP)
|
100% |
10420
1.94 KIPS |
|
|
Image Classification (Q)
|
97% |
12576
2.35 KIPS |
|
|
Image Segmentation (SP)
|
100% |
1474
23.9 IPS |
|
|
Image Segmentation (HP)
|
100% |
14701
238.3 IPS |
|
|
Image Segmentation (Q)
|
99% |
18178
294.7 IPS |
|
|
Pose Estimation (SP)
|
100% |
6628
7.73 IPS |
|
|
Pose Estimation (HP)
|
100% |
95156
111.0 IPS |
|
|
Pose Estimation (Q)
|
95% |
103131
120.9 IPS |
|
|
Object Detection (SP)
|
100% |
1593
126.4 IPS |
|
|
Object Detection (HP)
|
100% |
13153
1.04 KIPS |
|
|
Object Detection (Q)
|
91% |
15463
1.24 KIPS |
|
|
Face Detection (SP)
|
100% |
3139
37.3 IPS |
|
|
Face Detection (HP)
|
100% |
31176
370.4 IPS |
|
|
Face Detection (Q)
|
100% |
40846
485.3 IPS |
|
|
Depth Estimation (SP)
|
100% |
6076
46.8 IPS |
|
|
Depth Estimation (HP)
|
99% |
75351
580.5 IPS |
|
|
Depth Estimation (Q)
|
85% |
96541
754.8 IPS |
|
|
Style Transfer (SP)
|
100% |
18153
23.3 IPS |
|
|
Style Transfer (HP)
|
100% |
173715
223.3 IPS |
|
|
Style Transfer (Q)
|
96% |
172691
222.8 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
4092
151.1 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
31654
1.17 KIPS |
|
|
Image Super-Resolution (Q)
|
99% |
36667
1.35 KIPS |
|
|
Text Classification (SP)
|
100% |
2620
3.50 KIPS |
|
|
Text Classification (HP)
|
100% |
2314
3.09 KIPS |
|
|
Text Classification (Q)
|
93% |
2212
2.97 KIPS |
|
|
Machine Translation (SP)
|
100% |
3380
58.2 IPS |
|
|
Machine Translation (HP)
|
100% |
7293
125.6 IPS |
|
|
Machine Translation (Q)
|
98% |
8204
141.8 IPS |
