Overview
The article discusses the latest updates from Coral, including a partnership with balena, new open-source tools, and enhancements to their ML software stack. It highlights improvements in platform compatibility and development ease, particularly for IoT applications.
What You'll Learn
1
How to compile the Edge TPU runtime for unsupported platforms
2
How to use the new Windows drivers for Coral Mini PCIe and M.2 accelerators
3
Why using the Model Pipelining API can enhance ML model performance
4
How to leverage the new embedding extractor models for EfficientNet
5
When to use BodyPix for real-time person segmentation
Prerequisites & Requirements
- Basic understanding of machine learning concepts
- Familiarity with GitHub and basic command line usage(optional)
Key Questions Answered
What updates have been made to the Coral ML software stack?
The Coral ML software stack has seen several updates, including the release of the Edge TPU compiler version 14.1, a new Model Pipelining API for distributing models across multiple Edge TPUs, and new embedding extractor models for EfficientNet. These enhancements improve the performance and flexibility of machine learning applications on Coral devices.
How can I use Coral devices with Windows?
Coral now provides Windows drivers for Mini PCIe and M.2 accelerators, allowing users to prototype on Windows easily. This complements the existing support for the Coral USB Accelerator, enabling a smoother transition from development to production on the Windows platform.
What is the significance of the partnership between Coral and balena?
The partnership with balena enhances the deployment and management of ML-enabled devices at the edge. It simplifies the processes of provisioning, updating, and monitoring Coral devices, which is crucial for maintaining security and performance in large-scale IoT applications.
What is BodyPix and how can it be applied?
BodyPix is a Google person-segmentation model that allows for real-time understanding of human presence in camera frames. It can be used for applications like privacy-preserving population flow analysis and body-part segmentation, making it a versatile tool for various ML projects.
Technologies & Tools
Hardware
Edge Tpu
Used for accelerating ML applications at the edge.
Operating System
Mendel Linux
The latest version supports Coral devices with improved stability and compatibility.
Machine Learning Model
Efficientnet
Used for on-device backpropagation and classification tasks.
Machine Learning Model
Bodypix
Enables real-time person segmentation for various applications.
Key Actionable Insights
1Utilize the open-source Edge TPU runtime to customize your ML applications on unsupported platforms.This flexibility allows developers to integrate Coral's capabilities into their existing workflows, particularly for ARMv7 and RISC-V platforms, enabling broader application of ML solutions.
2Take advantage of the new Windows drivers for Coral devices to streamline your development process.By using these drivers, developers can quickly transition from prototyping to production, ensuring that their ML applications are efficient and effective on Windows systems.
3Explore the new Model Pipelining API to improve the performance of your ML models.This API allows for better resource utilization by distributing workloads across multiple Edge TPUs, which can significantly enhance processing speed and efficiency in complex ML tasks.
4Implement BodyPix in your projects for advanced person segmentation capabilities.This model can be particularly useful for applications requiring real-time analysis of human presence, such as in security or crowd management systems.
Common Pitfalls
1
Failing to keep Coral devices updated can lead to security vulnerabilities.
Regular updates are crucial for maintaining the integrity of ML applications and protecting sensitive data, especially in IoT deployments.
2
Overlooking the need for model retraining can degrade ML performance over time.
As new use cases emerge, it is essential to retrain models to ensure they remain accurate and effective in recognizing new patterns.
Related Concepts
Machine Learning
Iot
AI/ML
Edge Tpu
Tensorflow Lite