NASNet: Optimized for Qualcomm Devices

NASNet is a CNN-based architecture discovered via Neural Architecture Search (NAS) that can classify images from the Imagenet dataset.

This is based on the implementation of NASNet found here. This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the Qualcomm® AI Hub Models library to export with custom configurations. More details on model performance across various devices, can be found here.

Qualcomm AI Hub Models uses Qualcomm AI Hub Workbench to compile, profile, and evaluate this model. Sign up to run these models on a hosted Qualcomm® device.

Getting Started

There are two ways to deploy this model on your device:

Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

Runtime	Precision	Chipset	SDK Versions	Download
ONNX	float	Universal	QAIRT 2.42, ONNX Runtime 1.24.3	Download
ONNX	w8a8_mixed_fp16	Universal	QAIRT 2.42, ONNX Runtime 1.24.3	Download
QNN_DLC	float	Universal	QAIRT 2.45	Download
TFLITE	float	Universal	QAIRT 2.45	Download

For more device-specific assets and performance metrics, visit NASNet on Qualcomm® AI Hub.

Option 2: Export with Custom Configurations

Use the Qualcomm® AI Hub Models Python library to compile and export the model with your own:

• Custom weights (e.g., fine-tuned checkpoints)
• Custom input shapes
• Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for NASNet on GitHub for usage instructions.

Model Details

Model Type: Model_use_case.image_classification

Model Stats:

• Model checkpoint: nasnetalarge.tf_in1k
• Input resolution: 224x224
• GMACs: 5.9
• Activations (M): 19.4
• Number of parameters: 88.7M
• Model size (float): 338 MB

Performance Summary

Model	Runtime	Precision	Chipset	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit
NASNet	ONNX	float	Snapdragon® 8 Elite Gen 5 Mobile	8.372 ms	1 - 677 MB	NPU
NASNet	ONNX	float	Snapdragon® X2 Elite	8.998 ms	189 - 189 MB	NPU
NASNet	ONNX	float	Snapdragon® X Elite	17.761 ms	188 - 188 MB	NPU
NASNet	ONNX	float	Snapdragon® 8 Gen 3 Mobile	12.727 ms	1 - 834 MB	NPU
NASNet	ONNX	float	Qualcomm® QCS8550 (Proxy)	17.503 ms	0 - 194 MB	NPU
NASNet	ONNX	float	Qualcomm® QCS9075	27.992 ms	0 - 4 MB	NPU
NASNet	ONNX	float	Snapdragon® 8 Elite For Galaxy Mobile	10.678 ms	0 - 637 MB	NPU
NASNet	ONNX	w8a8_mixed_fp16	Snapdragon® 8 Elite Gen 5 Mobile	5.525 ms	11 - 389 MB	NPU
NASNet	ONNX	w8a8_mixed_fp16	Snapdragon® X2 Elite	5.675 ms	100 - 100 MB	NPU
NASNet	ONNX	w8a8_mixed_fp16	Snapdragon® X Elite	13.241 ms	98 - 98 MB	NPU
NASNet	ONNX	w8a8_mixed_fp16	Snapdragon® 8 Gen 3 Mobile	7.977 ms	12 - 515 MB	NPU
NASNet	ONNX	w8a8_mixed_fp16	Qualcomm® QCS8550 (Proxy)	11.416 ms	9 - 485 MB	NPU
NASNet	ONNX	w8a8_mixed_fp16	Qualcomm® QCS9075	13.77 ms	11 - 14 MB	NPU
NASNet	ONNX	w8a8_mixed_fp16	Snapdragon® 8 Elite For Galaxy Mobile	6.824 ms	12 - 391 MB	NPU
NASNet	QNN_DLC	float	Snapdragon® 8 Elite Gen 5 Mobile	9.064 ms	1 - 662 MB	NPU
NASNet	QNN_DLC	float	Snapdragon® X2 Elite	9.429 ms	1 - 1 MB	NPU
NASNet	QNN_DLC	float	Snapdragon® X Elite	17.926 ms	1 - 1 MB	NPU
NASNet	QNN_DLC	float	Snapdragon® 8 Gen 3 Mobile	12.289 ms	0 - 834 MB	NPU
NASNet	QNN_DLC	float	Qualcomm® QCS8275 (Proxy)	53.776 ms	1 - 666 MB	NPU
NASNet	QNN_DLC	float	Qualcomm® QCS8550 (Proxy)	17.107 ms	1 - 3 MB	NPU
NASNet	QNN_DLC	float	Qualcomm® QCS9075	29.112 ms	1 - 3 MB	NPU
NASNet	QNN_DLC	float	Qualcomm® QCS8450 (Proxy)	35.015 ms	0 - 808 MB	NPU
NASNet	QNN_DLC	float	Snapdragon® 8 Elite For Galaxy Mobile	10.42 ms	1 - 706 MB	NPU
NASNet	TFLITE	float	Snapdragon® 8 Elite Gen 5 Mobile	5.552 ms	0 - 575 MB	NPU
NASNet	TFLITE	float	Snapdragon® 8 Gen 3 Mobile	8.676 ms	0 - 711 MB	NPU
NASNet	TFLITE	float	Qualcomm® QCS8275 (Proxy)	43.227 ms	0 - 578 MB	NPU
NASNet	TFLITE	float	Qualcomm® QCS8550 (Proxy)	12.226 ms	0 - 3 MB	NPU
NASNet	TFLITE	float	Qualcomm® QCS9075	15.146 ms	0 - 191 MB	NPU
NASNet	TFLITE	float	Qualcomm® QCS8450 (Proxy)	28.859 ms	0 - 695 MB	NPU
NASNet	TFLITE	float	Snapdragon® 8 Elite For Galaxy Mobile	6.922 ms	0 - 562 MB	NPU

License

• The license for the original implementation of NASNet can be found here.

References

• Learning Transferable Architectures for Scalable Image Recognition
• Source Model Implementation

Community

• Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
• For questions or feedback please reach out to us.