『The Embedded Frontier』のカバーアート

The Embedded Frontier

The Embedded Frontier

著者： Jacob Beningo
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The Embedded Frontier, hosted by embedded systems expert Jacob Beningo, is a cutting-edge podcast dedicated to exploring the rapidly evolving world of embedded software and embedded system trends. Each episode delves into the latest technological advancements, industry standards, and innovative strategies that are shaping the future of embedded systems. Jacob Beningo, with his deep industry knowledge and experience, guides listeners through complex topics, making them accessible for both seasoned developers and newcomers alike.

This podcast serves as an educational platform, offering insights, interviews, and discussions with leading experts and innovators in the field. Listeners can expect to gain valuable knowledge on how to modernize their embedded software, implement best practices, and stay ahead in this dynamic and critical sector of technology. Whether you're an embedded software developer, a systems engineer, or simply a tech enthusiast, "The Embedded Frontier" is your go-to source for staying updated and inspired in the world of embedded systems. Join Jacob Beningo as he navigates the intricate and fascinating landscape of embedded technologies, providing a unique blend of technical expertise, industry updates, and practical advice.

© 2026 Beningo Embedded Group, LLC 経済学
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  • #026 - Metrics Dashboards to the Rescue

    #026 - Metrics Dashboards to the Rescue
    2026/03/18
    Jacob Beningo concludes his embedded software modernization framework series by discussing the establishment of metrics scoreboards for embedded systems development. He explores various metrics categories including code quality, performance, and security, with a deep dive into cyclomatic complexity as a powerful but underutilized metric among embedded developers.

    Key Takeaways:

    • Metrics scoreboards provide visibility into quality, predictability, and improvement opportunities in embedded development
    • Things that get measured get managed - tracking metrics helps identify root causes of development problems
    • Avoid vanity metrics like commit frequency; focus on metrics aligned with project goals
    • Cyclomatic complexity measures independent paths through functions and should stay below 10 for maintainable code
    • Seven key metrics categories: code quality, reliability/safety, testing, performance, resource utilization, process/productivity, and security
    • AI tools can significantly simplify dashboard creation and metrics analysis automation
    • Code coverage is valuable but can be gamed - ensure tests actually validate functionality, not just execute code
    • Compiler warnings should be eliminated or documented; aim for zero warnings in production code
    • Metrics trends are more important than point-in-time values for identifying code quality decay
    • Modern tools like Segger SystemView and Percepio Tracealyzer enable real-time performance metrics collection
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    33 分
  • #025 - Modernize Your Embedded System with AI / ML

    #025 - Modernize Your Embedded System with AI / ML
    2026/03/04
    Jacob Beningo explores how artificial intelligence and machine learning can modernize embedded systems development, sharing practical examples and dispelling myths about AI's applicability to embedded development. He demonstrates how AI can accelerate development workflows, from requirements gathering to code generation, while emphasizing an iterative, agile approach rather than fully autonomous AI development.

    Key Takeaways:

    • AI can provide 2x performance improvements for embedded development teams through automation and assistance
    • Use AI iteratively in small problem spaces rather than attempting fully autonomous agentic workflows
    • Create specialized AI tools for specific embedded systems domain areas like requirements gathering and code reviews
    • AI can help reduce debugging time, which typically consumes 20-40% of development effort
    • Machine learning inference can run effectively on resource-constrained 16-bit microcontrollers
    • Teams ignoring AI adoption risk being left behind as the technology becomes mainstream in embedded development
    • Focus on identifying repetitive daily tasks that can benefit from AI automation
    • Modern microcontrollers with neural processing units enable sophisticated on-chip machine learning applications
    • AI works best as a productivity multiplier rather than a replacement for embedded developers
    • Start with low-hanging fruit like debugging assistance and code review automation
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    33 分
  • #024 - Simulating Embedded Systems

    #024 - Simulating Embedded Systems
    2026/02/04
    In this episode, Jacob explores simulation for embedded systems as the seventh step in modernizing embedded development. He discusses functional simulation, on-chip simulation, and software architecture simulation, demonstrating how proper abstraction enables developers to validate code before hardware is available. Jacob provides a practical demonstration of simulating a push button module with debouncing algorithms, showing how modern software architecture can dramatically improve embedded system development workflows.

    Key Takeaways:

    • Functional simulation allows embedded developers to validate code without physical hardware by abstracting low-level hardware dependencies
    • Modern embedded architecture should decouple application code from hardware using zero-cost or low-cost abstractions
    • Push button debouncing serves as an excellent example for learning simulation techniques that every embedded engineer can relate to
    • On-chip simulation tools like QEMU and Renode can simulate processor-level instructions but may be incomplete for microcontroller peripherals
    • State machine simulation enables visual validation of system behavior before code deployment
    • AI tools can now rapidly generate simulation code, making functional simulation more accessible than ever
    • Socket communication provides deterministic and fast data exchange for functional simulation environments
    • Start simple with side projects like weather stations or button modules to learn simulation techniques
    • Legacy systems can be gradually refactored to support simulation by decoupling high-risk areas from hardware dependencies
    • Simulation enables faster development cycles and reduces dependency on hardware availability during early development phases
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    不明
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