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End-to-End Edge AI Deployment Stack

(Source: Link)

Table of Contents (ToC)

  1. Overview
  2. Choose the Right Edge Device
  3. Optimize Your Model
  4. Convert Your Model
  5. Deploy the Model
  6. Test and Validate
  7. Monitor and Update
  8. Tools and Frameworks
  9. Conclusion
  10. References

Overview

Edge deployment of AI models involves running machine learning algorithms on local hardware devices rather than in the cloud.

This approach offers benefits like:

  • reduced latency,
    • instead of waiting for a cloud server to process data, the data is processed locally on the edge device.
  • improved privacy,
    • the data is not sent to a remote server, so it is not exposed to the public internet.
  • offline functionality,
    • the device can operate without an internet connection.
  • real-time processing,
    • the data is processed immediately on the device without the need for a cloud server.

Edge-AI Deploying Workflow

Here's a step-by-step guide to deploying AI models on edge devices: 

graph LR
    A[Choose the Right Edge Device] --> B[Optimize Your Model]
    B --> C[Convert Your Model]
    C --> D[Deploy the Model]
    D --> E[Test and Validate]
    E --> F[Monitor and Update]

1. Choose the Right Edge Device

Select an edge device based on your project requirements:

  • Single-board computers: Raspberry Pi, NVIDIA Jetson Nano
  • Microcontrollers: Arduino, ESP32
  • Specialized AI hardware: Google Coral, Intel Neural Compute Stick
  • Mobile devices: Smartphones, tablets

Consider factors like processing power, memory, power consumption, and supported frameworks.

2. Optimize Your Model

Edge devices often have limited resources, so model optimization is crucial:

  • Quantization: Reduce model precision (e.g., from 32-bit to 8-bit)
  • Pruning: Remove unnecessary connections in neural networks
  • Knowledge Distillation(teacher-student approach): Create a smaller model that mimics a larger one
  • Model Compression: Use techniques like weight sharing or huffman coding

3. Convert Your Model

Convert your model to a format suitable for edge deployment:

  • TensorFlow Lite for TensorFlow models
  • ExecuTorch: for Pytorch models
  • CoreML for iOS devices
  • ONNX (Open Neural Network Exchange) for cross-framework compatibility
  • OpenVINO for Intel hardware

Example using TensorFlow Lite:

import tensorflow as tf
# Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()
# Save the model
with open('model.tflite', 'wb') as f:
f.write(tflite_model)

4. Set Up the Edge Device

Prepare your edge device:

  1. Install the necessary operating system (e.g., Raspbian for Raspberry Pi)
  2. Set up the development environment
  3. Install required libraries and drivers

5. Deploy the Model

Transfer the optimized model to the edge device and set up the inference pipeline:

  1. Copy the model file to the device
  2. Install the inference framework (e.g., TensorFlow Lite, OpenVINO)
  3. Write code to load the model and perform inference
import tflite_runtime.interpreter as tflite
# Load the model
interpreter = tflite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Perform inference
input_data = ... # Prepare your input data
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output_data = interpreter.get_tensor(output_details[0]['index'])

6. Optimize Performance

Fine-tune your deployment for better performance:

  • Use hardware acceleration if available (e.g., EdgeTPU, Neural Compute Stick)
  • Implement efficient data preprocessing on the device
  • Optimize the inference pipeline for your specific use case

7. Ensure Reliability and Maintenance

Implement strategies for reliable operation:

  • Error handling and logging
  • Over-the-air (OTA) updates for model and software
  • Monitoring and telemetry for performance tracking

8. Consider Security

Implement security measures:

  • Encrypt the model and sensitive data
  • Secure communication channels
  • Implement access controls and authentication

Tools and Frameworks

Several tools can help with edge deployment:

  • TensorFlow Lite: Optimized version of TensorFlow for mobile and embedded devices
  • ExecuTorch: An End-to-end solution for enabling on-device inference capabilities across mobile and edge devices
  • CoreML: Apple's machine learning framework for on-device model deployment
  • OpenVINO: Intel's toolkit for optimizing and deploying AI models
  • NVIDIA TensorRT: SDK for high-performance deep learning inference
  • Onnx: Open Neural Network Exchange for cross-platform compatibility.
  • Apache TVM: Open-source machine learning compiler framework

Platform & SW Suite: - Edge Impulse: Enables development and deployment of TinyML models - Qualcomm AI Hub: The platform for on-device AI - Imagimob AI DEEPCRAFT™ Studio: Edge AI development platform for Infineon Hardware - STM32Cube.AI: Free AI model optimizer for STM32

Conclusion

Deploying AI models on edge devices requires careful consideration of hardware limitations, model optimization, and efficient implementation.

By following this guide and leveraging appropriate tools, you can successfully run AI models on edge devices, enabling a wide range of applications in IoT, robotics, and embedded systems.

References

  • TensorFlow Lite: A lightweight version of TensorFlow designed for mobile and embedded devices.
  • ExecuTorch: A PyTorch-based execution framework for efficient model deployment across mobile and edge devices.
  • OpenVINO: Intel's toolkit for optimizing and deploying AI models on various hardware.
  • NVIDIA TensorRT: An SDK for high-performance deep learning inference on NVIDIA GPUs.
  • Edge Impulse: A platform for developing and deploying TinyML models on edge devices.
  • Onnx: An open format to represent deep learning models for interoperability between different frameworks.
  • Apache TVM: An open-source machine learning compiler framework for optimizing and deploying models.

Vision platforms:

  • Roboflow
  • viso.ai
  • Encord
  • Hugging Face
  • https://modelzoo.co/
  • https://www.customvision.ai/

Top 10 Companies developing AI Edge Solutions:

  • NVIDIA => Jetson
  • Google => Coral, ML Kit
  • Apple => CoreML:
    • coreML: https://developer.apple.com/videos/play/wwdc2024/10161/
    • Apple Intelligence: https://datasciencedojo.com/blog/apple-intelligence/
  • Amazon =>
    • AWS edge
    • AWS iot:
      • Bittle Robot Dog AWS IoT Showcase at Latency Conference 2023 - Perth, Australia: https://www.petoi.com/blogs/blog/bittle-robot-dog-aws-iot-showcase-at-latency-conference-2023-perth-australia?srsltid=AfmBOoo4AElmruyMlmWIIarJtCym2Aglg8kUcb8AI37-wMvET3GsxyD2
    • Inferentia
  • Infineon => Imagimob AI
  • Edge Impulse =>
  • MS => Azure => AI Edge => ONNX
  • tinyML =>
  • Facebooks => Meta => AI Edge => PyTorch Mobile
  • alibaba => AI Edge => X-brain
  • Baidu => EdgeBoard/DeepBench
  • IBM => Edge platform

SW Tools: - OpenVINO - TensorFlow Lite - PyTorch - ONNX - Apache TVM (compiler) - NVIDIA TensorRT - Edge Impulse/tinyML

HW Platforms: - Raspberry Pi 4 Model B - NVIDIA Jetson Nano - Arduino - ESP32 - Google Coral - Intel Neural Compute Stick - AURIX

Edges companies: https://stlpartners.com/articles/edge-computing/edge-computing-companies-2024/

On device AI/Embedded AI/Edge AI:

  • https://www.n-ix.com/on-device-ai/
  • https://www.forbes.com/consent/ketch/?toURL=https://www.forbes.com/councils/forbestechcouncil/2024/04/17/on-device-generative-ai-unlocks-true-smartphone-and-pc-value/

Why on-device AI Is the future of consumer and enterprise applications: https://www.computerweekly.com/opinion/Why-On-Device-AI-Is-the-future-of-consumer-and-enterprise-applications

26 Innovative Edge AI Companies You Should Know in 2024: https://www.omdena.com/blog/top-edge-ai-companies