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Restructure Alif E8 learning path with accurate ExecuTorch workflow
- Reorganized content into 9 logical pages following Docker-based workflow - Added: tool-installation, hardware-setup, docker-executorch-setup, model-export, build-runtime, alif-cmsis-project, flash-and-run, troubleshooting - Removed: outdated pages (boot-alif, install-setools, software-prerequisites, create-build-environment, mnist-inference) - Fixed all tabpane shortcodes to use code=true format with language attributes - Added images for hardware setup and macOS SETOOLS warnings - Updated _index.md with accurate skills and learning objectives
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‎content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-alif/1-overview.md‎

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---
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title: Overview
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weight: 2
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### FIXED, DO NOT MODIFY
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layout: learningpathall
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---
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## Hardware Overview - Alif's Ensemble E8 Series Board
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This Learning Path covers deploying PyTorch neural network models on the **Alif Ensemble E8 DevKit** using ExecuTorch with Ethos-U55 NPU acceleration.
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## What you'll build
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A complete pipeline to:
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1. Export PyTorch models to ExecuTorch format (`.pte`)
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2. Optimize models for Ethos-U55 NPU using Vela compiler
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3. Build the ExecuTorch runtime for Cortex-M55
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4. Deploy and run inference on Alif E8 hardware
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## Hardware Overview - Alif Ensemble E8 Series
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Selecting the best hardware for machine learning (ML) models depends on effective tools. You can visualize Arm Ethos-U85 performance early in the development cycle by using Alif's [Ensemble E8 Series Development Kit](https://alifsemi.com/ensemble-e8-series/).
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Selecting the best hardware for machine learning (ML) models depends on effective tools. You can visualize Arm Ethos-U performance early in the development cycle using Alif's [Ensemble E8 Series Development Kit](https://alifsemi.com/ensemble-e8-series/).
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<center>
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<iframe src='https://www.youtube.com/embed/jAvi2xKxkE4?si=Wd-E1PUCM4Y49uXM' allowfullscreen frameborder=0 width="800" height="400"></iframe>
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*Alif Ensemble Series Overview*
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</center>
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![Alif Ensemble E8 Board SoC Highlighted alt-text#center](./alif-ensemble-e8-board-soc-highlighted.jpg "Arm Ethos-U85 NPU location")
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![Alif Ensemble E8 Board SoC Highlighted alt-text#center](./alif-ensemble-e8-board-soc-highlighted.jpg "Arm Ethos-U NPU location")
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### Alif Ensemble E8 DevKit (DK-E8-Alpha)
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| Component | Specification |
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|-----------|---------------|
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| **CPU** | Arm Cortex-M55 (HE core @ 160MHz) |
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| **NPU** | Arm Ethos-U55 (128 MAC configuration) |
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| **ITCM** | 256 KB (fast instruction memory) |
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| **DTCM** | 256 KB (fast data memory) |
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| **SRAM0** | 4 MB (general purpose) |
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| **SRAM1** | 4 MB (NPU accessible) |
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| **MRAM** | 2-5.5 MB (non-volatile code storage) |
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{{% notice Note %}}
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The DK-E8-Alpha DevKit may use E7 silicon (AE722F80F55D5AS) which has 5.5MB MRAM and 13.5MB SRAM total. SETOOLS will auto-detect your actual chip variant. Always build for the detected silicon type.
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{{% /notice %}}
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### Alif's Ensemble E8 Processor Decoded
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|B4ADKA 2508|• B4ADKA - Assembly site & line identifier<br>• 2508 - year + week of manufacture (Week 08 of 2025)|
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|UASA37002.000.03|• UASA37002 - Identifies the silicon mask set<br>• .000.03 - means revision 3 of that mask|
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## Software Overview - Alif SETOOLS
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The [Alif Security Toolkit](https://swrm.alifsemi.com/Content/3.4%20SETOOLS.htm?TocPath=Secure%20Enclave%20Subsystem%7C_____4) (SETOOLS) contains utlities for working with the Alife Ensemble E8 board. You will install this later in this learning path on the following page: [Install Alif SETOOLS](/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-alif/3-install-setools/).
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## Software Overview - TinyML
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This Learning Path uses TinyML. TinyML is machine learning tailored to function on devices with limited resources, constrained memory, low power, and fewer processing capabilities.
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For a learning path focused on creating and deploying your own TinyML models, please see [Introduction to TinyML on Arm using PyTorch and ExecuTorch](/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/)
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## Benefits and Applications
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NPUs like Arm's [Ethos-U85](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85) provide significant advantages for embedded ML applications:
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- **Hardware Acceleration**: 10-50x faster inference compared to CPU-only execution
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- **Power Efficiency**: Lower power consumption per inference operation
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- **Real-time Capable**: Suitable for latency-sensitive applications
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- **On-device Processing**: No cloud dependency, enhanced privacy
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- **Visual Feedback**: RGB LED indicators provide immediate status confirmation
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- **Debug Capabilities**: UART and RTT output for detailed performance analysis
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The Alif [Ensemble E8 Series Development Kit](https://alifsemi.com/ensemble-e8-series/) integrates the Ethos-U85 NPU with Cortex-M55 and Cortex-A32 cores, making it ideal for prototyping TinyML applications that require both ML acceleration and general-purpose processing.
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## Software Stack
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```
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┌────────────────────────────────────────────────────┐
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│ Your Application │
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├────────────────────────────────────────────────────┤
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│ ExecuTorch Runtime │
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│ ├── Program Loader │
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│ ├── Executor │
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│ └── Memory Manager │
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├────────────────────────────────────────────────────┤
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│ Delegates & Kernels │
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│ ├── Ethos-U Delegate (NPU acceleration) │
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│ ├── Cortex-M Kernels (CPU fallback) │
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│ └── Quantized Kernels (INT8 ops) │
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├────────────────────────────────────────────────────┤
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│ Alif SDK / CMSIS │
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│ ├── Device HAL │
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│ ├── UART Driver │
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│ └── GPIO Driver │
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├────────────────────────────────────────────────────┤
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│ Hardware: Cortex-M55 + Ethos-U55 │
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└────────────────────────────────────────────────────┘
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```
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## Prerequisites
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### Required hardware
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- Alif Ensemble E8 DevKit (DK-E8-Alpha)
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- USB-C cable (connect to **PRG USB** port)
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- Optional: USB-to-Serial adapter for UART debugging
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### Required software
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| Tool | Version | Purpose |
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|------|---------|---------|
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| Docker | Latest | Development container |
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| Arm GCC | 13.x or 14.x | Cross-compiler |
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| CMSIS-Toolbox | 2.6.0+ | Build system |
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| J-Link | 7.x+ | Programming/debugging |
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| SETOOLS | 1.107.x | Alif flashing tools |
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| Python | 3.10+ | ExecuTorch export |
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## Key concepts
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### Model quantization
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ExecuTorch uses **INT8 quantization** for Ethos-U55:
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- Reduced memory footprint (4x smaller than FP32)
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- Faster inference on NPU
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- Minimal accuracy loss with proper calibration
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### Memory layout
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{{% notice Warning %}}
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Large tensors and model weights must be placed in **SRAM0** (4MB), not DTCM (256KB). Failing to do this causes linker overflow errors.
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{{% /notice %}}
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Place large buffers in SRAM0 using the section attribute:
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```c
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static uint8_t __attribute__((section(".bss.noinit"), aligned(16)))
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tensor_arena[512 * 1024]; // 512KB in SRAM0
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```
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### SRAM0 power management
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{{% notice Important %}}
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SRAM0 must be powered on before use via Secure Enclave services. Accessing unpowered SRAM causes HardFault crashes.
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{{% /notice %}}
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```c
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#include "se_services_port.h"
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#include "services_lib_api.h"
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uint32_t mem_error = 0;
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SERVICES_power_memory_req(
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se_services_s_handle,
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POWER_MEM_SRAM_0_ENABLE,
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&mem_error);
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```
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## Example: MNIST digit classification
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The included MNIST example demonstrates:
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- Loading a quantized CNN model (~100KB)
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- INT8 input preprocessing (28x28 grayscale image)
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- NPU-accelerated inference (~10-20ms)
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- Output processing (argmax of 10 classes)
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```
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Input: 28x28 grayscale image (784 bytes INT8)
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│
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▼
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┌─────────────────────────────────────────┐
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│ Conv2d(1→16) → ReLU → MaxPool │ NPU
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│ Conv2d(16→32) → ReLU → MaxPool │ accelerated
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│ Linear(1568→64) → ReLU │
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│ Linear(64→10) │
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└─────────────────────────────────────────┘
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│
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▼
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Output: 10 class scores (10 bytes INT8)
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```
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## Benefits and applications
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NPUs like Arm's [Ethos-U55](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u55) and [Ethos-U85](https://www.arm.com/products/silicon-ip-cpu/ethos/ethos-u85) provide significant advantages for embedded ML applications:
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- **Hardware acceleration**: 10-50x faster inference compared to CPU-only execution
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- **Power efficiency**: Lower power consumption per inference operation
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- **Real-time capable**: Suitable for latency-sensitive applications
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- **On-device processing**: No cloud dependency, enhanced privacy
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- **Visual feedback**: RGB LED indicators provide immediate status confirmation
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- **Debug capabilities**: UART and RTT output for detailed performance analysis
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The Alif [Ensemble E8 Series Development Kit](https://alifsemi.com/ensemble-e8-series/) integrates the Ethos-U55 NPU with Cortex-M55 and Cortex-A32 cores, making it ideal for prototyping TinyML applications that require both ML acceleration and general-purpose processing.

‎content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-alif/2-boot-alif.md‎

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