Profile Job Results
Jobs
jpvl4ryr5
Results Ready
Name
efficientnet_lite4
Target Device
- Arduino VENTUNO Q
- Ubuntu 24.04
- Qualcomm® Dragonwing™ IQ-8275 | QCS8275
Creator
ai-hub-support@qti.qualcomm.com
Input Specs
image_tensor: uint8[1, 300, 300, 3]Completion Time
9/19/2026, 1:25:17 PM
Options
--qairt_version latestVersions
- Lite RT: 1.4.2
- QAIRT: v2.50.0.260828221209
- QNN TfLite Delegate: v2.50.0.260828221209
- Qualcomm Linux: 24.04
- AI Hub: aihub-2026.09.11.0
Estimated Inference Time
1.02 ms
Estimated Peak Memory Usage
0 ‑ 18 MB
Compute Units
NPU
123
| Stage | Time | Memory |
|---|---|---|
First App Load | 2.36 s | 92‑93 MB |
Subsequent App Load | 237 ms | 19‑20 MB |
Inference | 1.02 ms | 0‑18 MB |
| Lite RT | 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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