Microcontroller Training in Bhutan

This instructor-led, live training in Bhutan (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.

Upon completing this training, participants will be able to:

• Comprehend the core principles of TinyML and its advantages for edge AI applications.
• Configure a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Enhance AI models for power efficiency and manage memory constraints effectively.

This instructor-led, live training in Bhutan (online or onsite) is designed for engineers looking to master embedded C programming for various microcontrollers across different processor architectures, including 8051, ARM CORTEX M-3, and ARM9.

In this instructor-led live training in Bhutan, participants will learn how to program the Arduino for real-world usage, such as to control lights, motors and motion detection sensors. This course assumes the use of real hardware components in a live lab environment (not 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.
• Add third-party components such as LCDs, accelerometers, gyroscopes, and GPS trackers to extend Arduino's functionality.
• Understand the various options in programming languages, from C to drag-and-drop languages.
• Test, debug, and deploy the Arduino to solve real world problems.

The RISC-V ecosystem has evolved from a specialized open-source Instruction Set Architecture (ISA) into a mainstream architecture, gaining significant momentum across edge computing, IoT, automotive sectors, AI acceleration, and server-grade processors. Industry analyses highlight a pressing talent gap: there are fewer than 5,000 RISC-V chip designers worldwide compared to an estimated 15,000 or more open positions in the semiconductor industry. Key hiring trends indicate that employers prioritize proficiency 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 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 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 unified skill set.