Fix - replaced *.png to *.webp to reduce file size

Author: kieranhejmadi01

content/learning-paths/servers-and-cloud-computing/performix-instruction-mix/how-to-3.md

@@ -14,11 +14,11 @@ Open Arm Performix and select the **Code Hotspots** recipe. If this is your firs
 
 Set the launch command to your baseline binary with the number of tokens (`-n`) set to 150. This value keeps startup overhead small compared to inference time, so the profile minimizes the time taken to load the model weights:
 
-![Arm Performix Code Hotspots recipe configuration showing launch arguments for the GPT-2 baseline run with -n 150 to emphasize inference runtime.#center](./code_hotspot.png "Code Hotspots recipe configuration for GPT-2 baseline")
+![Arm Performix Code Hotspots recipe configuration showing launch arguments for the GPT-2 baseline run with -n 150 to emphasize inference runtime.#center](./code_hotspot.webp "Code Hotspots recipe configuration for GPT-2 baseline")
 
 The results show that `kernels::matmul_ref()` is the hottest function. Double-clicking on the function with show which lines of source code the samples are mostly attributed to the accumulate step of `kernels::matmul_ref()`.
 
-![Arm Performix hotspot results table showing matmul_ref as the dominant runtime function during GPT-2 baseline inference.#center](./code_hotspot_results.png "Hotspot results highlighting matmul_ref")
+![Arm Performix hotspot results table showing matmul_ref as the dominant runtime function during GPT-2 baseline inference.#center](./code_hotspot_results.webp "Hotspot results highlighting matmul_ref")
 
 This confirms that matrix multiplication is the highest-impact optimization target.
 
@@ -44,19 +44,19 @@ Set the launch command to your baseline binary with the same runtime arguments u
 
 Use the same model and prompt arguments as your baseline terminal run so the measurements are comparable.
 
-![Arm Performix recipe setup screen showing Instruction Mix recipe selected with launch settings configured for the GPT-2 baseline executable.#center](./configuring-performix.png "Configure Arm Performix Instruction Mix recipe")
+![Arm Performix recipe setup screen showing Instruction Mix recipe selected with launch settings configured for the GPT-2 baseline executable.#center](./configuring-performix.webp "Configure Arm Performix Instruction Mix recipe")
 
 ### Analyze static disassembly
 
 After the run completes, review static disassembly first. This view is ordered by percentage contribution and provides a high-level profile of the application’s generated instruction stream. It can help you identify broad characteristics, such as whether the code is branch-heavy, dominated by memory operations, or making effective use of SIMD instructions. Use this static view to understand overall code generation patterns rather than to attribute performance to specific functions or source lines. Dynamic analysis is typically more relevant for optimization because it reflects the instructions that are actually executed at runtime.
 
-![Arm Performix static disassembly view showing instruction category breakdown for GPT-2 hot paths, highlighting scalar-heavy sections in baseline matmul code.#center](./static_disassembly.png "Static disassembly instruction classification")
+![Arm Performix static disassembly view showing instruction category breakdown for GPT-2 hot paths, highlighting scalar-heavy sections in baseline matmul code.#center](./static_disassembly.webp "Static disassembly instruction classification")
 
 ### Dynamic analysis
 
 Then inspect dynamic analysis bar chart to see where sampled runtime work is concentrated. Dynamic data is typically more useful for optimization because it reflects actual execution behavior for your input, runtime settings, and call frequencies.
 
-![Arm Performix dynamic functions table showing most runtime samples in matmul-related functions for baseline GPT-2 inference.#center](./instruction_mix_dynamic_analysis.png "Dynamic function sample distribution")
+![Arm Performix dynamic functions table showing most runtime samples in matmul-related functions for baseline GPT-2 inference.#center](./instruction_mix_dynamic_analysis.webp "Dynamic function sample distribution")
 
 Finally, in dynamic functions, you can break down operation types to individual functions. This is particularly useful when no single function dominates the profile, allowing you to inspect dynamic instruction patterns for specific functions.
 

content/learning-paths/servers-and-cloud-computing/performix-instruction-mix/how-to-4.md

@@ -64,7 +64,7 @@ args = [
 
 Restart your coding assistant, then prompt it to run Performix Instruction Mix and Code Hotspots on your `gpt2_user` binary and suggest Arm intrinsics improvements.
 
-![Screenshot of a coding assistant prompt configured to use Arm MCP Server tools for running Performix recipes and analyzing matmul_user optimization opportunities in the GPT-2 workload.#center](./mcp-performix-prompt.png "Coding assistant prompt for Performix analysis through Arm MCP Server")
+![Screenshot of a coding assistant prompt configured to use Arm MCP Server tools for running Performix recipes and analyzing matmul_user optimization opportunities in the GPT-2 workload.#center](./mcp-performix-prompt.webp "Coding assistant prompt for Performix analysis through Arm MCP Server")
 
 ## What you've learned and what's next