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README.md

@@ -34,8 +34,8 @@ 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 | 25.261 ms | 2 - 5 MB | FP16 | NPU |  [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 20.585 ms | 24 - 39 MB | FP16 | NPU |  [FFNet-54S.so](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 24.853 ms | 2 - 5 MB | FP16 | NPU |  [FFNet-54S.tflite](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 19.975 ms | 24 - 50 MB | FP16 | NPU |  [FFNet-54S.so](https://huggingface.co/qualcomm/FFNet-54S/blob/main/FFNet-54S.so) 
 
 
 ## Installation
@@ -96,16 +96,16 @@ python -m qai_hub_models.models.ffnet_54s.export
 ```
 Profile Job summary of FFNet-54S
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 25.26 ms
-Estimated Peak Memory Range: 2.43-4.68 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 18.42 ms
+Estimated Peak Memory Range: 0.44-107.92 MB
 Compute Units: NPU (113) | Total (113)
 
 Profile Job summary of FFNet-54S
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 20.59 ms
-Estimated Peak Memory Range: 24.04-39.17 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 14.57 ms
+Estimated Peak Memory Range: 146.99-207.62 MB
 Compute Units: NPU (176) | Total (176)
 
 
@@ -211,7 +211,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of FFNet-54S can be found
   [here](https://github.com/Qualcomm-AI-research/FFNet/blob/master/LICENSE).
-- 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
 * [Simple and Efficient Architectures for Semantic Segmentation](https://arxiv.org/abs/2206.08236)