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README.md

@@ -38,7 +38,7 @@ 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 | 4.738 ms | 0 - 32 MB | INT8 | NPU |  [Yolo-NAS-Quantized.tflite](https://huggingface.co/qualcomm/Yolo-NAS-Quantized/blob/main/Yolo-NAS-Quantized.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 4.77 ms | 0 - 193 MB | INT8 | NPU |  [Yolo-NAS-Quantized.tflite](https://huggingface.co/qualcomm/Yolo-NAS-Quantized/blob/main/Yolo-NAS-Quantized.tflite) 
 
 
 
@@ -101,8 +101,8 @@ python -m qai_hub_models.models.yolonas_quantized.export
 Profile Job summary of Yolo-NAS-Quantized
 --------------------------------------------------
 Device: RB3 Gen 2 (Proxy) (12)
-Estimated Inference Time: 14.08 ms
-Estimated Peak Memory Range: 0.14-60.40 MB
+Estimated Inference Time: 13.74 ms
+Estimated Peak Memory Range: 0.09-66.41 MB
 Compute Units: NPU (203),CPU (1) | Total (204)
 
 
@@ -146,8 +146,8 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
 
 ## License
 - The license for the original implementation of Yolo-NAS-Quantized can be found
-  [here](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.YOLONAS.md).
-- 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)
+  [here](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.md).
+- The license for the compiled assets for on-device deployment can be found [here](https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.md)
 
 ## References
 * [YOLO-NAS by Deci Achieves SOTA Performance on Object Detection Using Neural Architecture Search](https://deci.ai/blog/yolo-nas-object-detection-foundation-model/)