AI at the Edge

Spis treści książki

• Foreword
• Preface
  • About This Book
  • What to Expect
  • What You Need to Know Already
  • Responsible, Ethical, and Effective AI
  • Further Resources
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. A Brief Introduction to Edge AI
  • Defining Key Terms
    • Embedded
    • The Edge (and the Internet of Things)
    • Artificial Intelligence
    • Machine Learning
    • Edge AI
    • Embedded Machine Learning and Tiny Machine Learning
    • Digital Signal Processing
  • Why Do We Need Edge AI?
    • To Understand the Benefits of Edge AI, Just BLERP
      • Bandwidth
      • Latency
      • Economics
      • Reliability
      • Privacy
      • Using BLERP
    • Edge AI for Good
    • Key Differences Between Edge AI and Regular AI
      • Training on the edge is rare
      • The focus of edge AI is on sensor data
      • ML models can get very small
      • Learning from feedback is limited
      • Compute is diverse and heterogeneous
      • Good enough is often the goal
      • Tools and best practices are still evolving
  • Summary
• 2. Edge AI in the Real World
  • Common Use Cases for Edge AI
    • Greenfield and Brownfield Projects
    • Real-World Products
      • Preventing forest fires using power line fault detection
      • Protecting first responders with intelligent wearables
      • Understanding elephant behavior with smart collars
  • Types of Applications
    • Keeping Track of Objects
      • Key benefits for object tracking
    • Understanding and Controlling Systems
      • Key benefits for understanding and controlling systems
    • Understanding People and Living Things
      • Key benefits for understanding people and living things
    • Transforming Signals
      • Key benefits for transforming signals
  • Building Applications Responsibly
    • Responsible Design and AI Ethics
    • Black Boxes and Bias
    • Technology That Harms, Not Helps
      • The costs of negligence
      • Mitigating societal harms
  • Summary
• 3. The Hardware of Edge AI
  • Sensors, Signals, and Sources of Data
    • Types of Sensors and Signals
    • Acoustic and Vibration
    • Visual and Scene
    • Motion and Position
    • Force and Tactile
    • Optical, Electromagnetic, and Radiation
    • Environmental, Biological, and Chemical
    • Other Signals
  • Processors for Edge AI
    • Edge AI Hardware Architecture
    • Microcontrollers and Digital Signal Processors
      • Low-end MCUs
      • High-end MCUs
      • Digital signal processors (DSPs)
    • System-on-Chip
    • Deep Learning Accelerators
    • FPGAs and ASICs
    • Edge Servers
    • Multi-Device Architectures
    • Devices and Workloads
  • Summary
• 4. Algorithms for Edge AI
  • Feature Engineering
    • Working with Data Streams
    • Digital Signal Processing Algorithms
      • Resampling
      • Filtering
      • Spectral analysis
      • Image feature detection
    • Combining Features and Sensors
  • Artificial Intelligence Algorithms
    • Algorithm Types by Functionality
      • Classification
      • Regression
      • Object detection and segmentation
      • Anomaly detection
      • Clustering
      • Dimensionality reduction
      • Transformation
    • Algorithm Types by Implementation
      • Conditionals and heuristics
      • Classical machine learning
      • Deep learning
      • Combining algorithms
      • Postprocessing algorithms
      • Fail-safe design
    • Optimization for Edge Devices
      • Choice of algorithm
      • Compression and optimization
    • On-Device Training
  • Summary
• 5. Tools and Expertise
  • Building a Team for AI at the Edge
    • Domain Expertise
    • Diversity
    • Stakeholders
    • Roles and Responsibilities
      • Knowledge and understanding
      • Planning and execution
      • Algorithm development
      • Product engineering
      • Technical services
    • Hiring for Edge AI
    • Learning Edge AI Skills
      • Practice
      • Theory
  • Tools of the Trade
    • Software Engineering
      • Operating systems
      • Programming and scripting languages
      • Dependency management
      • Containerization
      • Distributed computing
      • Cloud providers
    • Working with Data
      • Data capture
      • IoT device management
      • Data storage and management
      • Data pipelines
    • Algorithm Development
      • Mathematical and scientific computing libraries
      • Data visualization
      • Interactive computing environments
      • Digital signal processing
      • Deep learning frameworks
      • Model compression and optimization
      • Experiment tracking
      • Automated machine learning (AutoML)
      • Machine learning operations (MLOps)
    • Running Algorithms On-Device
      • Math and DSP libraries
      • Machine learning inference
      • On-device learning
    • Embedded Software Engineering and Electronics
      • Embedded hardware tools
      • Development boards
      • Embedded software tools
      • Emulators and simulators
      • Embedded Linux
      • Automated hardware testing
    • End-to-End Platforms for Edge AI
  • Summary
• 6. Understanding and Framing Problems
  • The Edge AI Workflow
    • Responsible AI in the Edge AI Workflow
  • Do I Need Edge AI?
    • Describing a Problem
    • Do I Need to Deploy to the Edge?
      • Things that dont work well on the edge
      • Disadvantages of edge compute
    • Do I Need Machine Learning?
      • Reasons to use ML
      • The drawbacks of ML
      • Knowing when to use ML
    • Practical Exercise
  • Determining Feasibility
    • Moral Feasibility
    • Business Feasibility
      • Proving benefit
      • Understanding constraints
    • Dataset Feasibility
    • Technological Feasibility
      • Framing problems
      • Device capabilities and solution choice
    • Making a Final Decision
    • Planning an Edge AI Project
      • Defining acceptable performance
      • Understanding time and resource constraints
  • Summary
• 7. How to Build a Dataset
  • What Does a Dataset Look Like?
  • The Ideal Dataset
  • Datasets and Domain Expertise
  • Data, Ethics, and Responsible AI
    • Minimizing Unknowns
    • Ensuring Domain Expertise
  • Data-Centric Machine Learning
  • Estimating Data Requirements
    • A Practical Workflow for Estimating Data Requirements
  • Getting Your Hands on Data
    • The Unique Challenges of Capturing Data at the Edge
  • Storing and Retrieving Data
    • Getting Data into Stores
    • Collecting Metadata
  • Ensuring Data Quality
    • Ensuring Representative Datasets
    • Reviewing Data by Sampling
    • Label Noise
      • Avoiding label noise
    • Common Data Errors
    • Drift and Shift
    • The Uneven Distribution of Errors
  • Preparing Data
    • Labeling
      • Not all problems require labels
      • Semi-supervised and active learning algorithms
      • Bias in labeling
      • Labeling tools
        • Annotation tools
        • Crowdsourced labeling
        • Assisted and automated labeling
        • Semi-supervised and active learning
    • Formatting
    • Data Cleaning
      • Auditing your dataset
      • Fixing issues
        • Amending values
        • Substituting values
        • Excluding records
      • Evaluation and automation
      • Fixing balance issues
    • Feature Engineering
    • Splitting Your Data
      • How is data split?
      • Pitfalls when splitting data
    • Data Augmentation
    • Data Pipelines
  • Building a Dataset over Time
  • Summary
• 8. Designing Edge AI Applications
  • Product and Experience Design
    • Design Principles
    • Scoping a Solution
    • Setting Design Goals
      • Systemic goals
      • Technical goals
      • Values-based design goals
  • Architectural Design
    • Hardware, Software, and Services
    • Basic Application Architectures
      • Basic flow
      • Ensemble flow
      • Parallel flow
      • Series flow
      • Cascading flow
      • Sensor fusion flow
    • Complex Application Architectures and Design Patterns
      • Heterogeneous cascade
      • Multi-device cascade
      • Cascade to the cloud
      • Intelligent gateway
      • Human-in-the-loop
    • Working with Design Patterns
  • Accounting for Choices in Design
    • Design Deliverables
  • Summary
• 9. Developing Edge AI Applications
  • An Iterative Workflow for Edge AI Development
    • Exploration
    • Goal Setting
      • Calling it quits
    • Bootstrapping
      • Why bootstrapping is helpful
      • Developing a baseline algorithm
      • Our first hardware
      • Responsible AI review
    • Test and Iterate
      • Feedback loops
      • Iterations in practice
        • Updating your plans
      • Ethical AI review
    • Deployment
    • Support
  • Summary
• 10. Evaluating, Deploying, and Supporting Edge AI Applications
  • Evaluating Edge AI Systems
    • Ways to Evaluate a System
      • Evaluating individual components
      • Evaluating integrated systems
      • Simulated real-world testing
      • Real-world testing
        • Quality assurance testing
        • Usability testing
      • Monitoring a deployed system
    • Useful Metrics
      • Algorithmic performance
        • Loss
        • Accuracy
        • Confusion matrix
        • Precision and recall
        • Positive and negative rates
        • F1 score and MCC
        • ROC and AUC
        • Error metrics
        • Mean average precision
      • Computational and hardware performance
        • Memory
        • Floating-point operations (FLOPs)
        • Latency
        • Duty cycle
        • Energy
        • Thermal
    • Techniques for Evaluation
    • Evaluation and Responsible AI
  • Deploying Edge AI Applications
    • Predeployment Tasks
    • Mid-Deployment Tasks
    • Postdeployment Tasks
  • Supporting Edge AI Applications
    • Postdeployment Monitoring
      • Types of feedback from deployed systems
        • Data samples
        • Distribution changes
        • Application metrics
        • Outcomes
        • User reports
    • Improving a Live Application
      • Solving problems using feedback
      • Refining an algorithm over time
      • Supporting multiple deployed algorithms
    • Ethics and Long-Term Support
      • Performance degradation
      • New information
      • Evolving cultural norms
      • Changing legal standards
  • What Comes Next
• 11. Use Case: Wildlife Monitoring
  • Problem Exploration
  • Solution Exploration
  • Goal Setting
  • Solution Design
    • What Solutions Already Exist?
    • Solution Design Approaches
    • Design Considerations
    • Environmental Impact
    • Bootstrapping
    • Define Your Machine Learning Classes
  • Dataset Gathering
    • Edge Impulse
      • Public project
    • Choose Your Hardware and Sensors
      • Hardware configuration
    • Data Collection
      • Connecting your device directly to Edge Impulse for data collection
    • iNaturalist
    • Dataset Limitations
    • Dataset Licensing and Legal Obligations
    • Cleaning Your Dataset
    • Uploading Data to Edge Impulse
  • DSP and Machine Learning Workflow
    • Digital Signal Processing Block
    • Machine Learning Block
      • Visual mode
      • EON Tuner
  • Testing the Model
    • Live Classification
    • Model Testing
    • Test Your Model Locally
  • Deployment
    • Create Library
    • Mobile Phone and Computer
    • Prebuilt Binary Flashing
    • Impulse Runner
    • GitHub Source Code
  • Iterate and Feedback Loops
  • AI for Good
  • Related Works
    • Datasets
    • Research
• 12. Use Case: Food Quality Assurance
  • Problem Exploration
  • Solution Exploration
  • Goal Setting
  • Solution Design
    • What Solutions Already Exist?
    • Solution Design Approaches
    • Design Considerations
    • Environmental and Social Impact
    • Bootstrapping
    • Define Your Machine Learning Classes
  • Dataset Gathering
    • Edge Impulse
      • Edge Impulse public project
    • Choose Your Hardware and Sensors
      • Hardware configuration
    • Data Collection
    • Data Ingestion Firmware
    • Uploading Data to Edge Impulse
    • Cleaning Your Dataset
    • Dataset Licensing and Legal Obligations
  • DSP and Machine Learning Workflow
    • Digital Signal Processing Block
    • Machine Learning Block
      • Visual mode
  • Testing the Model
    • Live Classification
    • Model Testing
  • Deployment
    • Prebuilt Binary Flashing
    • GitHub Source Code
  • Iterate and Feedback Loops
  • Related Works
    • Research
    • News and Other Articles
• 13. Use Case: Consumer Products
  • Problem Exploration
  • Goal Setting
  • Solution Design
    • What Solutions Already Exist?
    • Solution Design Approaches
    • Design Considerations
    • Environmental and Social Impact
    • Bootstrapping
    • Define Your Machine Learning Classes
  • Dataset Gathering
    • Edge Impulse
      • Edge Impulse public project
    • Choose Your Hardware and Sensors
      • Hardware configuration
    • Data Collection
    • Data Ingestion Firmware
      • Mobile phone
      • nRF Edge Impulse mobile phone application
    • Cleaning Your Dataset
    • Dataset Licensing and Legal Obligations
  • DSP and Machine Learning Workflow
    • Digital Signal Processing Block
    • Machine Learning Blocks
      • Visual mode
      • Anomaly detection
  • Testing the Model
    • Live Classification
    • Model Testing
  • Deployment
    • Prebuilt Binary Flashing
    • GitHub Source Code
  • Iterate and Feedback Loops
  • Related Works
    • Research
    • News and Other Articles
• Index

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