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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 | 22.812 ms | 2 - 5 MB | FP16 | NPU |  [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) 
-| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 17.334 ms | 24 - 43 MB | FP16 | NPU |  [FFNet-40S.so](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.so) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 23.048 ms | 0 - 29 MB | FP16 | NPU |  [FFNet-40S.tflite](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.tflite) 
+| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 17.363 ms | 24 - 42 MB | FP16 | NPU |  [FFNet-40S.so](https://huggingface.co/qualcomm/FFNet-40S/blob/main/FFNet-40S.so) 
 
 
 ## Installation
@@ -94,23 +94,6 @@ device. This script does the following:
 python -m qai_hub_models.models.ffnet_40s.export
 ```
 
-```
-Profile Job summary of FFNet-40S
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 16.60 ms
-Estimated Peak Memory Range: 0.02-101.51 MB
-Compute Units: NPU (92) | Total (92)
-
-Profile Job summary of FFNet-40S
---------------------------------------------------
-Device: Samsung Galaxy S24 (14)
-Estimated Inference Time: 12.56 ms
-Estimated Peak Memory Range: 24.04-82.64 MB
-Compute Units: NPU (140) | Total (140)
-
-
-```
 ## How does this work?
 
 This [export script](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/FFNet-40S/export.py)