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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 | 10.46 ms | 1 - 3 MB | FP16 | NPU |  [FFNet-122NS-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-122NS-LowRes/blob/main/FFNet-122NS-LowRes.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 10.778 ms | 6 - 38 MB | FP16 | NPU |  [FFNet-122NS-LowRes.so](https://huggingface.co/qualcomm/FFNet-122NS-LowRes/blob/main/FFNet-122NS-LowRes.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 10.407 ms | 0 - 2 MB | FP16 | NPU |  [FFNet-122NS-LowRes.tflite](https://huggingface.co/qualcomm/FFNet-122NS-LowRes/blob/main/FFNet-122NS-LowRes.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 10.785 ms | 6 - 37 MB | FP16 | NPU |  [FFNet-122NS-LowRes.so](https://huggingface.co/qualcomm/FFNet-122NS-LowRes/blob/main/FFNet-122NS-LowRes.so) 
 
 
 ## Installation
@@ -96,16 +96,16 @@ python -m qai_hub_models.models.ffnet_122ns_lowres.export
 ```
 Profile Job summary of FFNet-122NS-LowRes
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 10.46 ms
-Estimated Peak Memory Range: 0.61-2.78 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 7.37 ms
+Estimated Peak Memory Range: 0.61-55.46 MB
 Compute Units: NPU (216) | Total (216)
 
 Profile Job summary of FFNet-122NS-LowRes
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 10.78 ms
-Estimated Peak Memory Range: 6.04-37.62 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 7.63 ms
+Estimated Peak Memory Range: 6.02-82.00 MB
 Compute Units: NPU (349) | Total (349)
 
 
@@ -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-122NS-LowRes 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)