Advanced Embedded Systems Development Training Course

In this instructor-led live training in the Netherlands, participants will learn how to program Arduino for real-world applications, such as controlling lights, motors, and motion detection sensors. This course requires the use of actual hardware components in a live lab environment, rather than software-simulated hardware.

By the end of this training, participants will be able to:

• Program Arduino to control lights, motors, and other devices.
• Understand Arduino's architecture, including inputs and connectors for add-on devices.
• Integrate third-party components such as LCDs, accelerometers, gyroscopes, and GPS trackers to extend Arduino's functionality.
• Understand the various programming language options, ranging from C to drag-and-drop interfaces.
• Test, debug, and deploy Arduino solutions to address real-world problems.

This instructor-led live training, available online or at your location, is designed for C developers who want to master the design principles specific to embedded C.

Upon completion of this course, participants will be able to:

• Grasp the design considerations that ensure embedded C programs are reliable
• Clearly define the functionality of an embedded system
• Determine the program logic and structure required to achieve desired outcomes
• Design robust, error-free embedded applications
• Maximize performance on target hardware platforms

Course Format:

• Interactive lectures and discussions
• Practical exercises and hands-on practice
• Live-lab implementation for real-world experience

Customization Options:

• For customized training arrangements, please contact us.

This instructor-led, live training in the Netherlands (online or onsite) targets intermediate-level embedded systems engineers and AI developers aiming to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its advantages for edge AI applications.
• Configuring a development environment suitable for TinyML projects.
• Training, optimizing, and deploying AI models on low-power microcontrollers.
• Leveraging TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Enhancing AI models for improved power efficiency and adherence to memory limitations.

The RISC-V ecosystem has evolved from a niche open-source instruction set architecture (ISA) into a mainstream platform gaining significant traction across edge computing, IoT, automotive, AI acceleration, and server-class processors. Industry analyses highlight a pressing talent gap: with fewer than 5,000 RISC-V chip designers worldwide, there are over 15,000 open positions in the semiconductor sector. Key hiring trends indicate that employers prioritize candidates proficient in RISC-V architecture alongside SoC design, RTL verification (UVM/SystemVerilog), AI accelerator development, Rust systems programming, confidential computing, and open-source toolchain expertise. The most rapidly growing competency areas include automotive-grade RISC-V (ISO 26262), server-class processors featuring AIA interrupt controllers and multi-core coherence, and edge AI inference NPUs. Companies such as SiFive, Qualcomm, and Western Digital are accelerating RISC-V development, increasing demand for engineers capable of bridging architecture specification, silicon implementation, firmware, and software stack development within a unified skill set.