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README.md

@@ -33,7 +33,7 @@ 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 | 2.508 ms | 0 - 3 MB | FP16 | NPU |  [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 2.539 ms | 0 - 4 MB | INT8 | NPU |  [HRNetPoseQuantized.tflite](https://huggingface.co/qualcomm/HRNetPoseQuantized/blob/main/HRNetPoseQuantized.tflite) 
 
 
 ## Installation
@@ -94,9 +94,9 @@ python -m qai_hub_models.models.hrnet_pose_quantized.export
 ```
 Profile Job summary of HRNetPoseQuantized
 --------------------------------------------------
-Device: Samsung Galaxy S23 Ultra (13)
-Estimated Inference Time: 2.51 ms
-Estimated Peak Memory Range: 0.02-3.47 MB
+Device: Samsung Galaxy S24 (14)
+Estimated Inference Time: 1.86 ms
+Estimated Peak Memory Range: 0.02-97.61 MB
 Compute Units: NPU (515) | Total (515)
 
 
@@ -216,7 +216,7 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 ## License
 - The license for the original implementation of HRNetPoseQuantized 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
 * [Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1902.09212)