Profile Job Results
Jobs
jgdoql66p
Results Ready
Name
convnext_tiny_float
Target Device
- SA8650 (Proxy)
- Android 13
- Qualcomm® SA8650P
Creator
ai-hub-support@qti.qualcomm.com
Target Model
Input Specs
image_tensor: float32[1, 224, 224, 3]Completion Time
10/26/2025, 5:31:37 AM
Options
--qairt_version latestVersions
- TensorFlow Lite: 2.17.0
- QAIRT: v2.39.0.250925215840_163802
- QNN TfLite Delegate: v2.39.0.250925215840_163802
- Android: 13 (TP1A.220624.014)
- AI Hub: aihub-2025.10.21.0
Estimated Inference Time
2.94 ms
Estimated Peak Memory Usage
0 ‑ 440 MB
Compute Units
NPU
328
| Stage | Time | Memory |
|---|---|---|
First App Load | 2.69 s | 504‑505 MB |
Subsequent App Load | 2.61 s | 536‑538 MB |
Inference | 2.94 ms | 0‑440 MB |
| TensorFlow Lite | Value |
|---|---|
| number_of_threads | 4 |
| QNN Delegate | Value |
|---|---|
| backend_type | kHtpBackend |
| log_level | kLogLevelWarn |
| htp_options.performance_mode | kHtpBurst |
| htp_options.precision | kHtpFp16 |
| htp_options.useConvHmx | true |
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