Advanced Embedded Systems Development Training Course

In this instructor-led, live training in Botswana, participants will learn how to programme Arduino for practical applications, such as controlling lights, motors, and motion detection sensors. This course assumes the use of real hardware components within a live laboratory environment (excluding software-simulated hardware).

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

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

This instructor-led training, available online or on-site, is tailored for C developers seeking to master the design principles of embedded C.

Upon completing this training, participants will be capable of:

• Gaining insight into the design factors that ensure the reliability of embedded C programs
• Articulating the functionality of an embedded system
• Establishing the program logic and structure necessary to achieve desired outcomes
• Creating a robust and error-free embedded application
• Achieving peak performance from the target hardware

Course Format:

• Interactive lectures and discussions
• Practical exercises
• Hands-on implementation within a live-lab setting

Customization Options:

• For inquiries regarding customized training for this course, please contact us.

This instructor-led, live training in Botswana (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Understand the fundamentals of TinyML and its benefits for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• 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 technology with substantial market momentum across edge computing, the Internet of Things (IoT), automotive systems, artificial intelligence acceleration, and server-class processors. Industry reports highlight a critical talent shortage: there are fewer than 5,000 RISC-V chip designers worldwide against an estimated 15,000 or more open positions in the semiconductor industry. Key hiring trends indicate that employers prioritise proficiency in RISC-V architecture alongside skills in SoC design, RTL verification (UVM/SystemVerilog), AI accelerator development, Rust systems programming, confidential computing, and open-source toolchain expertise. The fastest-growing competency areas include automotive-grade RISC-V (ISO 26262), server-class processors (AIA interrupt controllers, multi-core coherence), and edge AI inference NPUs. Companies such as SiFive, Qualcomm, and Western Digital have accelerated their RISC-V development efforts, driving demand for engineers capable of bridging architecture specification, silicon implementation, firmware, and software stack development within a single skill set.