Bring the hackathon branch up to date with main

.gitignore

@@ -74,12 +74,13 @@ tmp/
 fvp_stdout.log
 
 # Editor config. The CMSIS Solution extension regenerates tasks.json,
-# settings.json and cmsis.json; only the FVP run/debug wiring is kept: the
-# model shim + its Dockerfile, and launch.json, whose Arm-FVP launch config is
-# "manual" (see the comment in it). The leading negation overrides a global
-# excludes file that ignores every .vscode directory.
+# settings.json, cmsis.json and launch.json; only the macOS FVP wiring is kept:
+# the model shim + its Dockerfile, and launch.json.mac, an Arm-FVP launch
+# config that waits for the model in Docker (see the comment in it). The
+# leading negation overrides a global excludes file that ignores every .vscode
+# directory.
 !.vscode/
 .vscode/*
 !.vscode/fvp.sh
 !.vscode/fvp.Dockerfile
-!.vscode/launch.json
+!.vscode/launch.json.mac

.vscode.d/tasks.json

@@ -31,10 +31,10 @@
             "label": "Create AI layer",
             "detail": "Export model/model.py for the NPU in cmsis-executorch.cbuild-mlops.yml and write ai_layer/ (run a CMSIS build or 'cbuild setup' first)",
             "type": "shell",
-            "command": "python3",
+            "command": "${workspaceFolder}/.venv/bin/python",
             "args": ["create_ai_layer.py", "cmsis-executorch.cbuild-mlops.yml"],
             "windows": {
-                "command": "python"
+                "command": "${workspaceFolder}\\.venv\\Scripts\\python.exe"
             },
             "options": {
                 "cwd": "${workspaceFolder}"
@@ -112,7 +112,7 @@
         {
             "id": "setupPythonVersion",
             "type": "promptString",
-            "description": "Python version for uv (>=3.10,<3.15), e.g. 3.12 or 3.12.10",
+            "description": "Python version for uv (>=3.10,<3.14), e.g. 3.12 or 3.12.10",
             "default": "3.12"
         }
     ]

.vscode/fvp.sh

@@ -1,8 +1,9 @@
 #!/usr/bin/env bash
-# Stand-in for the FVP_Corstone_SSE-320 executable, referenced from the
-# `debugger: model:` node of the SSE-320-U85 target-set. The CMSIS Solution
-# extension runs it both as the "CMSIS Run" task and as the gdbtarget debug
-# server, in the latter case with
+# Stand-in for the FVP_Corstone_SSE-320 executable on macOS, where the
+# `debugger: model:` node of the SSE-320-U85 target-set points at it (see the
+# README). The CMSIS Solution extension runs it both as the "CMSIS Run" task
+# and as the gdbtarget debug server (.vscode/launch.json.mac), in the latter
+# case with
 #
 #   -D --plugin $AVH_FVP_PLUGINS/GDBServer.so -C GDBServer.port=3333 \
 #      -f board/Corstone-320/fvp_config.txt --simlimit 60 -a <application>.hex
@@ -15,8 +16,8 @@
 #   * runs the model in Docker on macOS, where Arm publishes no FVP build, with
 #     the GDB port forwarded to the host so arm-none-eabi-gdb can reach it.
 #
-# On Linux it just execs the real model. On Windows, where the extension cannot
-# run a bash script, point the csolution's `model:` at FVP_Corstone_SSE-320.exe.
+# On Linux it just execs the real model. Windows and Linux need no shim: the
+# csolution's `model:` names FVP_Corstone_SSE-320 directly.
 set -euo pipefail
 
 MODEL="${FVP_MODEL:-FVP_Corstone_SSE-320}"

.vscode/launch.json → .vscode/launch.json.mac

@@ -2,6 +2,10 @@
     "version": "0.2.0",
     "configurations": [
         {
+            // macOS only: copy this file to .vscode/launch.json after pointing
+            // the csolution's `debugger: model:` at .vscode/fvp.sh. On Windows
+            // and Linux the extension generates a working launch.json itself.
+            //
             // Generated by the CMSIS Solution extension from its Arm-FVP adapter
             // template, then switched to "manual" because two "target" fields
             // differ from the generated ones and would be lost on regeneration:

README.md

@@ -13,14 +13,14 @@ itself is in [documentation/example.md](documentation/example.md).
 
 1. Install [VS Code](https://code.visualstudio.com/).
 2. Install the extensions **Keil Studio Pack** (`Arm.keil-studio-pack`, which
-   brings the CMSIS Solution extension 1.70.0 or newer that the project's
-   task drop-ins need) and **Python** (`ms-python.python`). Sign in with an
+   brings the CMSIS Solution extension 1.72.0 or newer with CMSIS-Toolbox
+   2.15) and **Python** (`ms-python.python`). Sign in with an
    Arm account when Keil Studio asks; the free Keil MDK Community license is
    enough.
 3. Nothing else by hand: when you open the project, the Arm Tools Environment
    Manager offers to install the tools pinned in `vcpkg-configuration.json`
    (CMSIS-Toolbox, Arm Compiler 6, GCC, CMake, Ninja, and on Linux and
-   Windows the Corstone-320 FVP; macOS runs it in Docker, see step 8). Accept.
+   Windows the Corstone-320 FVP; macOS runs it in Docker, see step 6). Accept.
 4. Optional: the **CMSIS Developer Assistant** extension lets an AI agent
    (Claude Code or GitHub Copilot Chat) build, flash and debug the board
    through an MCP server. Install it, install one of the agents, run
@@ -111,8 +111,8 @@ Repeat this after another project has reprogrammed the table.
        Arch:       v2.0.0
        MACs/cc:    256
        Cmd stream: v1
-   ExecuTorch Ethos-U85 example: 8896 byte model
-   Output: 10 element(s): 0.0079 0.0459 0.0475 -0.0475 0.0791 0.0411 -0.0285 -0.0744 -0.2246 -0.0016
+   ExecuTorch Ethos-U85 example: 8736 byte model
+   Output: 10 element(s): -0.0142 0.0206 0.0601 -0.0886 0.0791 0.0522 -0.0301 -0.0775 -0.2800 -0.0095
    Test_result: PASS
    ```
 
@@ -122,10 +122,11 @@ Repeat this after another project has reprogrammed the table.
    the output logits."
 
 **FVP instead of the board:** choose the **SSE-320-U85** target-type and click
-**Run** or **Debug**; the same output appears in the terminal. On macOS the
-FVP runs in Docker (Docker Desktop must be running; the first run builds the
-image, about 100 MB). On Windows set `model:` in the csolution's SSE-320-U85
-target-set to `FVP_Corstone_SSE-320`.
+**Run** or **Debug**; the same output appears in the terminal. On macOS,
+where Arm ships no FVP build, the FVP runs in Docker: set `model:` in the
+csolution's SSE-320-U85 target-set to `${workspaceFolder}/.vscode/fvp.sh`,
+copy `.vscode/launch.json.mac` to `.vscode/launch.json`, and keep Docker
+Desktop running (the first run builds the image, about 100 MB).
 
 ## 7. If something does not work
 
@@ -143,10 +144,11 @@ target-set to `FVP_Corstone_SSE-320`.
   before the switch was moved. Set `UART4` and reopen the port.
 - **`app-write-mram` gets no answer:** press reset while it waits, check SW4
   is on `SEUART`, close any terminal holding the port.
-- **"torch is not installed":** run the task **Setup Python virtual
+- **"`.venv` does not exist":** run the task **Setup Python virtual
   environment** first; it is only needed to regenerate the AI layer.
-- After **Apply**, the extension adds a J-Link entry to `.vscode/launch.json`
-  next to the committed FVP entry. That is expected.
+- `.vscode/launch.json` is generated by the extension for the target-types
+  and is not committed; after **Apply** it has a J-Link entry for the
+  DevKit-E8. That is expected.
 
 ## Read on
 

RTE/_Debug_DevKit-E8/Pre_Include_Global.h

@@ -1,6 +1,6 @@
 /*
  * CSOLUTION generated file: DO NOT EDIT!
- * Generated by: csolution version 2.14.1+p38-gf512b381
+ * Generated by: csolution version 2.15.1+p3-gf46d68bf
  *
  * Project: 'cmsis-executorch.Debug+DevKit-E8' 
  * Target:  'Debug+DevKit-E8' 
@@ -12,10 +12,10 @@
 /* ARM::Machine Learning:NPU Support:Ethos-U Driver&Generic U85@1.26.2 */
 // enabling global pre includes 
         #define ETHOSU_ARCH u85
-/* PyTorch::Machine Learning:ExecuTorch:Backend EthosU@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Backend EthosU@1.5.1 */
 #define EXECUTORCH_BUILD_ARM_BAREMETAL 1
         #define ET_USE_ETHOS_U_BACKEND 1
-/* PyTorch::Machine Learning:ExecuTorch:Runtime@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Runtime@1.5.1 */
 /* ExecuTorch global configuration */
         #define C10_USING_CUSTOM_GENERATED_MACROS
         #define FLATBUFFERS_MAX_ALIGNMENT 1024

RTE/_Debug_DevKit-E8/RTE_Components.h

@@ -1,6 +1,6 @@
 /*
  * CSOLUTION generated file: DO NOT EDIT!
- * Generated by: csolution version 2.14.1+p38-gf512b381
+ * Generated by: csolution version 2.15.1+p3-gf46d68bf
  *
  * Project: 'cmsis-executorch.Debug+DevKit-E8' 
  * Target:  'Debug+DevKit-E8' 
@@ -30,17 +30,17 @@
 #define RTE_Drivers_USART               /* Driver UART  */
 /* AlifSemiconductor::Device:SOC Peripherals:PINCONF@2.2.0 */
 #define RTE_Drivers_LL_PINCONF              /* Driver PinPAD and PinMux */
-/* PyTorch::Machine Learning:ExecuTorch Operators:Quantized dequantize@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch Operators:Quantized dequantize@1.5.1 */
 #define RTE_ML_EXECUTORCH_OP_QUANTIZED_DEQUANTIZE     /* ExecuTorch op_dequantize */
-/* PyTorch::Machine Learning:ExecuTorch Operators:Quantized quantize@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch Operators:Quantized quantize@1.5.1 */
 #define RTE_ML_EXECUTORCH_OP_QUANTIZED_QUANTIZE     /* ExecuTorch op_quantize */
-/* PyTorch::Machine Learning:ExecuTorch:Backend EthosU@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Backend EthosU@1.5.1 */
 #define RTE_ML_EXECUTORCH_BACKEND_ETHOS_U     /* ExecuTorch Ethos-U Backend (Cortex-M host) */
-/* PyTorch::Machine Learning:ExecuTorch:Kernel Registration@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Kernel Registration@1.5.1 */
 #define RTE_ML_EXECUTORCH_KERNEL_REGISTRATION     /* ExecuTorch Kernel Registration */
-/* PyTorch::Machine Learning:ExecuTorch:Kernel Utils@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Kernel Utils@1.5.1 */
 #define RTE_ML_EXECUTORCH_KERNEL_UTILS     /* ExecuTorch Kernel Utils */
-/* PyTorch::Machine Learning:ExecuTorch:Runtime@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Runtime@1.5.1 */
 #define RTE_ML_EXECUTORCH_RUNTIME     /* ExecuTorch Runtime */
 
 

RTE/_Debug_SSE-320-U85/Pre_Include_Global.h

@@ -12,10 +12,10 @@
 /* ARM::Machine Learning:NPU Support:Ethos-U Driver&Generic U85@1.26.2 */
 // enabling global pre includes 
         #define ETHOSU_ARCH u85
-/* PyTorch::Machine Learning:ExecuTorch:Backend EthosU@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Backend EthosU@1.5.1 */
 #define EXECUTORCH_BUILD_ARM_BAREMETAL 1
         #define ET_USE_ETHOS_U_BACKEND 1
-/* PyTorch::Machine Learning:ExecuTorch:Runtime@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Runtime@1.5.1 */
 /* ExecuTorch global configuration */
         #define C10_USING_CUSTOM_GENERATED_MACROS
         #define FLATBUFFERS_MAX_ALIGNMENT 1024

RTE/_Debug_SSE-320-U85/RTE_Components.h

@@ -30,17 +30,17 @@
 #define RTE_TIMEOUT      1
 /* ARM::Machine Learning:NPU Support:Ethos-U Driver&Generic U85@1.26.2 */
 #define RTE_ETHOS_U_CORE_DRIVER
-/* PyTorch::Machine Learning:ExecuTorch Operators:Quantized dequantize@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch Operators:Quantized dequantize@1.5.1 */
 #define RTE_ML_EXECUTORCH_OP_QUANTIZED_DEQUANTIZE     /* ExecuTorch op_dequantize */
-/* PyTorch::Machine Learning:ExecuTorch Operators:Quantized quantize@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch Operators:Quantized quantize@1.5.1 */
 #define RTE_ML_EXECUTORCH_OP_QUANTIZED_QUANTIZE     /* ExecuTorch op_quantize */
-/* PyTorch::Machine Learning:ExecuTorch:Backend EthosU@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Backend EthosU@1.5.1 */
 #define RTE_ML_EXECUTORCH_BACKEND_ETHOS_U     /* ExecuTorch Ethos-U Backend (Cortex-M host) */
-/* PyTorch::Machine Learning:ExecuTorch:Kernel Registration@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Kernel Registration@1.5.1 */
 #define RTE_ML_EXECUTORCH_KERNEL_REGISTRATION     /* ExecuTorch Kernel Registration */
-/* PyTorch::Machine Learning:ExecuTorch:Kernel Utils@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Kernel Utils@1.5.1 */
 #define RTE_ML_EXECUTORCH_KERNEL_UTILS     /* ExecuTorch Kernel Utils */
-/* PyTorch::Machine Learning:ExecuTorch:Runtime@1.4.1 */
+/* PyTorch::Machine Learning:ExecuTorch:Runtime@1.5.1 */
 #define RTE_ML_EXECUTORCH_RUNTIME     /* ExecuTorch Runtime */
 
 

ai_layer/ai_layer.clayer.yml

@@ -6,7 +6,7 @@ layer:
   description: TinyCNN int8 image classifier for Ethos-U85
 
   packs:
-    - pack: PyTorch::ExecuTorch@1.4.1
+    - pack: PyTorch::ExecuTorch@1.5.1
 
   define:
     - ET_LOG_ENABLED: 0