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README.md

@@ -38,7 +38,8 @@ More details on model performance across various devices, can be found
 
 | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
 | ---|---|---|---|---|---|---|---|
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.24 ms | 0 - 1 MB | FP16 | NPU |  [MobileNet-v2-Quantized.tflite](https://huggingface.co/qualcomm/MobileNet-v2-Quantized/blob/main/MobileNet-v2-Quantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 0.237 ms | 0 - 1 MB | INT8 | NPU |  [MobileNet-v2-Quantized.tflite](https://huggingface.co/qualcomm/MobileNet-v2-Quantized/blob/main/MobileNet-v2-Quantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.352 ms | 0 - 90 MB | INT8 | NPU |  [MobileNet-v2-Quantized.so](https://huggingface.co/qualcomm/MobileNet-v2-Quantized/blob/main/MobileNet-v2-Quantized.so) 
 
 
 ## Installation
@@ -98,11 +99,18 @@ python -m qai_hub_models.models.mobilenet_v2_quantized.export
 ```
 Profile Job summary of MobileNet-v2-Quantized
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 0.24 ms
-Estimated Peak Memory Range: 0.01-1.49 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.17 ms
+Estimated Peak Memory Range: 0.01-34.29 MB
 Compute Units: NPU (70) | Total (70)
 
+Profile Job summary of MobileNet-v2-Quantized
+--------------------------------------------------
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 0.25 ms
+Estimated Peak Memory Range: 0.16-34.32 MB
+Compute Units: NPU (69) | Total (69)
+
 
 ```
 ## How does this work?
@@ -220,7 +228,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of MobileNet-v2-Quantized can be found
   [here](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf).
-- The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf).
+- The license for the compiled assets for on-device deployment can be found [here]({deploy_license_url})
 
 ## References
 * [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)