The Embedded Frontier

In this episode of Embedded Frontier, Jacob Beningo discusses the top trends in embedded systems for 2025. He highlights the increasing role of AI and machine learning, the dominance of open-source software, the critical importance of security, and the ongoing relevance of programming languages like C and C++. Beningo also emphasizes the rise of simulation technologies, the integration of DevOps and observability, and the expansion of edge AI, providing insights into how these trends will shape the future of embedded systems development. Takeaways ·      AI will enhance embedded systems development. ·      Machine learning applications are still underutilized. ·      Open-source software is becoming increasingly dominant. ·      Security is a top priority for embedded systems. ·      C remains the most widely used programming language. ·      Simulation technologies will modernize development processes. ·      DevOps practices are essential for improving software quality. ·      Edge AI will allow for local data processing. ·      Modern programming languages will see increased adoption. ·      2025 will bring significant changes to embedded systems. Keywords embedded systems, AI, machine learning, open source, security, programming languages, DevOps, edge AI, simulation technologies

Curious about the future of embedded systems? In this episode, Jacob Beningo is joined by Shawn Prestridge from IAR Systems to tackle the pressing challenges developers face today. From real-world insights into AI’s role in embedded devices to the overlooked pitfalls of security in connected systems, Shawn shares practical lessons learned from his decades in the field. Whether you’re interested in how AI can transform your development process, or you're navigating the complexities of modern code quality and security, this conversation offers actionable strategies that every embedded developer needs. Don't miss this chance to learn from the cutting edge of embedded systems development.

In this episode, Jacob Beningo interviews François Baldassari, Memfault CEO, about IoT security compliance demands. They discuss embedded manufacturers' readiness for new security regulations, the challenges they face, and potential solutions. They also explore the differences between the EU's Cyber Resilience Act and the US's Cyber Trust Mark. François emphasizes the importance of OTA updates, using open-source software, and building security teams within hardware companies. He also highlights the need for collecting the right data and observability to improve security posture. Takeaways * Embedded manufacturers are not fully ready for new IoT security compliance demands. * Regulatory frameworks like the EU's Cyber Resilience Act and the US's Cyber Trust Mark are coming into effect and will require certification of cybersecurity guidelines. * Challenges include the uncertainty of the regulations, the additional costs and effort required, and the lack of established infrastructure and best practices. * Recommendations for compliance include implementing OTA updates, using open-source software, adopting SBOM scanning, and ensuring observability of devices. * AI is not currently a solution for compliance, but it may play a role in the future as more data is collected and analyzed. * Joining the conversation around open-source products and following security best practices can help improve device security.

Summary In this conversation, Jacob and Daniel Situnayake discuss the future of AI and machine learning in embedded software development. They explore the challenges and opportunities of implementing AI and machine learning at the edge, and how tools like TensorFlow Lite for Microcontrollers and Edge Impulse are making it easier for developers to deploy models on resource-constrained devices. They also discuss the importance of balancing model accuracy with resource constraints and the potential for AI-generated models in the future. Overall, the conversation highlights the growing interest and potential of AI and machine learning in the embedded space. Keywords AI, machine learning, embedded software development, TensorFlow Lite, Edge Impulse, resource constraints, model accuracy, AI-generated models Takeaways * AI and machine learning are being increasingly applied to embedded software development, opening up new possibilities for edge devices. * Tools like TensorFlow Lite for Microcontrollers and Edge Impulse are making it easier for developers to implement AI and machine learning on resource-constrained devices. * Balancing model accuracy with resource constraints is a key consideration in embedded AI development. * The future of embedded AI and machine learning holds the potential for AI-generated models and more sophisticated applications at the edge.

In this episode, Jacob Beningo discusses the importance of debugging in embedded development and shares several techniques to decrease debugging time. He highlights the statistic that development teams spend 20-40% of their time debugging, which equates to 2.5-4.5 man-months of development. Beningo emphasizes the use of test-driven development (TDD) as a way to prevent bugs and decrease debugging time. He also recommends mastering debugging techniques for microcontrollers, using profiling and monitoring tools, employing assertions, and utilizing on-host simulation. Beningo concludes by encouraging listeners to track their debugging time and implement strategies to decrease it. Takeaways * Development teams spend 20-40% of their time debugging, which can equate to 2.5-4.5 man months of development. * Test-driven development (TDD) can help prevent bugs and decrease debugging time. * Mastering debugging techniques for microcontrollers and utilizing profiling and monitoring tools can improve debugging efficiency. * Using assertions and on-host simulation are additional techniques to decrease debugging time. * Tracking debugging time and implementing strategies to decrease it can lead to increased productivity and innovation.