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README.md

@@ -36,7 +36,7 @@ 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 | 6.736 ms | 1 - 3 MB | FP16 | NPU |  [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 6.741 ms | 1 - 27 MB | FP16 | NPU |  [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) 
 
 
 ## Installation
@@ -96,9 +96,9 @@ python -m qai_hub_models.models.ddrnet23_slim.export
 ```
 Profile Job summary of DDRNet23-Slim
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 6.74 ms
-Estimated Peak Memory Range: 0.95-3.10 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 4.64 ms
+Estimated Peak Memory Range: 0.04-65.76 MB
 Compute Units: NPU (131) | Total (131)
 
 
@@ -218,7 +218,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of DDRNet23-Slim can be found
   [here](https://github.com/chenjun2hao/DDRNet.pytorch/blob/main/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
 * [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://arxiv.org/abs/2101.06085)