Profile Job Results
Jobs
jgnevx4kg
Results Ready
Name
bisenet_float
Target Device
- SA8650 (Proxy)
- Android 13
- Qualcomm® SA8650P
Creator
ai-hub-support@qti.qualcomm.com
Target Model
Input Specs
image: float32[1, 720, 960, 3]Completion Time
1/11/2026, 7:26:13 AM
Options
--qairt_version latestVersions
- TensorFlow Lite: 2.17.0
- QAIRT: v2.41.0.251128145156_191518
- QNN TfLite Delegate: v2.41.0.251128145156_191518
- Android: 13 (TP1A.220624.014)
- AI Hub: aihub-2026.01.05.0
Estimated Inference Time
28.3 ms
Estimated Peak Memory Usage
32 ‑ 38 MB
Compute Units
NPU
64
| Stage | Time | Memory |
|---|---|---|
First App Load | 3.30 s | 321‑322 MB |
Subsequent App Load | 207 ms | 25‑27 MB |
Inference | 28.3 ms | 32‑38 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.optimization_strategy | kHtpOptimizeForInferenceO3 |
| htp_options.useConvHmx | true |
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