Advanced Embedded Systems Development Training Course

In this instructor-led, live training in Vietnam, participants will learn how to program Arduino for practical applications, such as controlling lights, motors, and motion detection sensors. This course requires the use of actual hardware components within 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 its inputs and connectors for add-on devices.
• Integrate third-party components such as LCDs, accelerometers, gyroscopes, and GPS trackers to expand Arduino's functionality.
• Explore various programming language options, ranging from C to drag-and-drop languages.
• Test, debug, and deploy Arduino solutions to address real-world problems.

This instructor-led, live training session (available online or onsite) is designed for C developers seeking to master embedded C design principles.

Upon completion of this training, participants will be equipped to:

• Grasp the design considerations essential for creating reliable embedded C programs
• Define the functionality of an embedded system
• Establish program logic and structure to achieve desired outcomes
• Design robust, error-free embedded applications
• Achieve optimal performance from target hardware

Course Format:

• Interactive lectures and discussions
• Exercises and practical practice
• Hands-on implementation within a live-lab environment

Customization Options:

• For information on requesting a customized training program, please contact us to arrange your needs.

This instructor-led, live training in Vietnam (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers looking to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Understand the fundamental principles of TinyML and its benefits for edge AI applications.
• Establish a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Apply TensorFlow Lite and Edge Impulse to build real-world TinyML solutions.
• Optimize AI models for power efficiency and memory constraints.

The RISC-V ecosystem has evolved from a niche open-source instruction set architecture (ISA) into a mainstream standard with strong market momentum across edge computing, IoT, automotive systems, AI acceleration, and server-grade processors. Industry analyses highlight a pressing talent gap: there are fewer than 5,000 RISC-V chip designers worldwide, yet over 15,000 positions remain open in the semiconductor sector. Key hiring trends indicate that employers seek candidates proficient in RISC-V architecture combined with SoC design, RTL verification (UVM/SystemVerilog), AI accelerator development, Rust systems programming, confidential computing, and open-source toolchain skills. The fastest-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 their RISC-V initiatives, driving demand for engineers capable of integrating architecture specification, silicon implementation, firmware, and software stack development within a unified skill set.