Overview
The article discusses the advancements made by Coral in 2020 to enhance accessibility to edge AI technologies. It highlights new product releases, improvements in ML APIs, and showcases various use cases that demonstrate the practical applications of Coral's offerings.
What You'll Learn
1
How to integrate the Coral Accelerator Module into custom designs
2
Why model pipelining improves the performance of edge AI applications
3
When to use pre-trained models like MobileDet for object detection
Prerequisites & Requirements
- Basic understanding of machine learning concepts
- Familiarity with TensorFlow and Coral products(optional)
Key Questions Answered
What new products did Coral introduce in 2020?
In 2020, Coral introduced the Coral Accelerator Module and the Dev Board Mini. The Accelerator Module combines the Edge TPU with power circuitry, while the Dev Board Mini offers a more compact and efficient design for evaluating projects.
How does Coral simplify the development process for edge AI?
Coral has streamlined the ML workflow by enhancing its APIs, making the C++ API modular and performant, and introducing a new model partitioner that improves performance by up to 10x. This allows developers to create more efficient edge AI applications.
What are some use cases for Coral's edge AI technology?
Coral's technology has been used in various applications, including smart water meters to prevent water loss, agricultural systems to improve harvest yield, and AI-powered noise cancellation in meeting kits.
How does the new version of Coral ML APIs enhance performance?
The new version of Coral ML APIs aligns the C++ API with Python, eliminates unnecessary abstractions, and introduces a model partitioner that automatically optimizes models based on profiling, significantly enhancing performance.
Technologies & Tools
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Hardware
Edge Tpu
Used to accelerate AI at the edge in Coral products.
Software
Tensorflow
Framework used for developing and deploying machine learning models with Coral.
Key Actionable Insights
1Consider integrating the Coral Accelerator Module into your custom hardware designs to leverage edge AI capabilities.This module simplifies the integration process with its PCIe and USB2 interfaces, making it suitable for various applications in IoT and AI.
2Utilize pre-trained models like MobileDet for efficient object detection in your projects.These models are optimized for mobile systems and can significantly reduce development time while improving accuracy in AI applications.
3Explore the new model pipelining features in Coral's ML APIs to enhance the performance of your edge AI applications.By using model pipelining, you can achieve better resource utilization and faster inference times, which is crucial for real-time applications.
Common Pitfalls
1
Overlooking the importance of model optimization when deploying AI applications at the edge.
Without proper optimization, applications may not perform efficiently, leading to slower response times and increased resource consumption.
Related Concepts
Edge AI Technologies
Machine Learning Model Optimization
Coral Product Ecosystem