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README.md

@@ -35,8 +35,8 @@ 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 | 22.739 ms | 2 - 5 MB | FP16 | NPU |  [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 17.313 ms | 24 - 49 MB | FP16 | NPU |  [FFNet-40S.so](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 22.513 ms | 2 - 5 MB | FP16 | NPU |  [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 17.466 ms | 24 - 46 MB | FP16 | NPU |  [FFNet-40S.so](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.so) 
 
 
 ## Installation
@@ -97,16 +97,16 @@ python -m qai_hub_models.models.ffnet_40s.export
 ```
 Profile Job summary of FFNet-40S
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 22.74 ms
-Estimated Peak Memory Range: 2.45-4.77 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 16.61 ms
+Estimated Peak Memory Range: 0.06-95.83 MB
 Compute Units: NPU (92) | Total (92)
 
 Profile Job summary of FFNet-40S
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 17.31 ms
-Estimated Peak Memory Range: 24.04-48.93 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 12.68 ms
+Estimated Peak Memory Range: 24.02-78.73 MB
 Compute Units: NPU (141) | Total (141)
 
 
@@ -212,7 +212,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of FFNet-40S 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)