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README.md

@@ -35,8 +35,8 @@ 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 | 0.917 ms | 0 - 6 MB | FP16 | NPU |  [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.31 ms | 0 - 84 MB | FP16 | NPU |  [Shufflenet-v2.so](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.29 ms | 0 - 7 MB | FP16 | NPU |  [Shufflenet-v2.tflite](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 0.797 ms | 1 - 65 MB | FP16 | NPU |  [Shufflenet-v2.so](https://huggingface.co/qualcomm/Shufflenet-v2/blob/main/Shufflenet-v2.so) 
 
 
 ## Installation
@@ -93,23 +93,6 @@ device. This script does the following:
 python -m qai_hub_models.models.shufflenet_v2.export
 ```
 
-```
-Profile Job summary of Shufflenet-v2
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.58 ms
-Estimated Peak Memory Range: 0.01-32.20 MB
-Compute Units: NPU (202) | Total (202)
-
-Profile Job summary of Shufflenet-v2
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 0.22 ms
-Estimated Peak Memory Range: 0.01-46.52 MB
-Compute Units: NPU (156) | Total (156)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/Shufflenet-v2/export.py)