Deploying Edge and Embedded AI Systems with Heather Gorr - #655
Nov 13, 2023
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Heather Gorr, Principal MATLAB Product Marketing Manager at MathWorks, dives into the fascinating world of deploying AI models for embedded systems. She emphasizes crucial factors like data preparation, device constraints, and latency requirements for successful implementation. Heather shares insights on MLOps techniques to enhance deployment speed, while tailoring AI solutions for industries such as automotive and oil & gas. Anecdotes of real-world AI applications illustrate the importance of rigorous validation processes and interdisciplinary collaboration in ensuring safety and reliability.
Understanding device requirements and tailoring data processing and preparation is crucial for successful deployment of AI models to hardware devices.
Simulation plays a significant role in testing and verifying the robustness of AI models, allowing for exploration of edge cases and evaluation of model behavior in various scenarios without costly physical tests.
Deep dives
Considerations for Deploying Machine Learning Models to Hardware Devices
When deploying machine learning models to hardware devices, it is crucial to think ahead and consider the specific requirements of the device. Starting with the end in mind, understanding the inference requirements and device capabilities is essential. Data processing and preparation should also be tailored to the device, taking into account factors such as latency and data types. Simulation plays a significant role in testing and verifying the model's robustness, including addressing adversarial examples. Quantization and precision requirements must be considered, and tools like MATLAB can help with this process. Testing and validation in different phases, including software and hardware, are necessary to ensure the model works in real-world conditions. Continuous integration and continuous development practices can aid in maintaining and updating the model over time, considering new data and improvements. Specialized MLOps techniques are emerging in the embedded systems field, combining classic MLOps approaches with hardware testing methods.
Advantages of Simulations in Model Testing for Hardware Deployment
Simulations are highly valuable for testing machine learning models before deploying them to hardware devices. They allow engineers to explore different edge cases and evaluate the behavior of the model in various scenarios without the need for costly physical tests. By simulating adversarial examples and failure modes, engineers can ensure that the model is robust and resilient. Simulation also aids in the verification and validation process, contributing to the overall confidence and safety of the model. It is crucial to align the simulation closely with the real-world system and consider factors such as data streams, latency, and performance requirements.
Considerations for Model Life Cycle in Embedded Systems
Once a machine learning model is deployed to a hardware device, the model's life cycle begins. Updating and maintaining the model becomes essential as new data and research emerge. This includes adjusting the model based on new information, improving its performance, and addressing any undesired behavior. Continuous integration and continuous development practices help facilitate updates and ensure that the model remains effective and up-to-date. Considerations also arise regarding caching the model, retaining certain information, and dealing with data updates. Overall, the model life cycle in embedded systems requires ongoing attention and adaptation to ensure optimal performance and safety.
Integration of Hardware Testing and MLOps for Embedded Systems
Integrating hardware testing and MLOps techniques is essential for successful deployment of machine learning models in embedded systems. Combining best practices from both domains enables efficient testing, performance evaluation, continuous integration, and continuous development. MLOps practices, such as unit testing, continuous testing, and continuous integration, ensure the reliability and robustness of the model. Hardware testing techniques, including model-in-the-loop, software-in-the-loop, processor-in-the-loop, and hardware-in-the-loop (PIL, SIL, PIL, and HIL) testing, are crucial for validating the model's performance in the actual devices and system environment. Collaboration among different experts, such as data scientists, hardware engineers, and test engineers, is essential for successful integration and deployment of machine learning models in embedded systems.
Today we’re joined by Heather Gorr, principal MATLAB product marketing manager at MathWorks. In our conversation with Heather, we discuss the deployment of AI models to hardware devices and embedded AI systems. We explore factors to consider during data preparation, model development, and ultimately deployment, to ensure a successful project. Factors such as device constraints and latency requirements which dictate the amount and frequency of data flowing onto the device are discussed, as are modeling needs such as explainability, robustness and quantization; the use of simulation throughout the modeling process; the need to apply robust verification and validation methodologies to ensure safety and reliability; and the need to adapt and apply MLOps techniques for speed and consistency. Heather also shares noteworthy anecdotes about embedded AI deployments in industries including automotive and oil & gas.
The complete show notes for this episode can be found at twimlai.com/go/655.
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