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README.md

@@ -32,8 +32,8 @@ 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 | 1.324 ms | 0 - 15 MB | FP16 | NPU |  [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.01 ms | 0 - 8 MB | FP16 | NPU |  [QuickSRNetSmall.so](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.316 ms | 0 - 8 MB | FP16 | NPU |  [QuickSRNetSmall.tflite](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.01 ms | 0 - 49 MB | FP16 | NPU |  [QuickSRNetSmall.so](https://huggingface.co/qualcomm/QuickSRNetSmall/blob/main/QuickSRNetSmall.so) 
 
 
 ## Installation
@@ -90,23 +90,6 @@ device. This script does the following:
 python -m qai_hub_models.models.quicksrnetsmall.export
 ```
 
-```
-Profile Job summary of QuickSRNetSmall
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.94 ms
-Estimated Peak Memory Range: 0.02-17.17 MB
-Compute Units: NPU (8),CPU (3) | Total (11)
-
-Profile Job summary of QuickSRNetSmall
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.62 ms
-Estimated Peak Memory Range: 0.22-13.52 MB
-Compute Units: NPU (11) | Total (11)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/QuickSRNetSmall/export.py)