Online Microcontroller Training in Malaysia

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

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

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Establish a development environment for TinyML projects.
• Train, optimise, and deploy AI models on low-power microcontrollers.
• Utilise TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Optimize AI models to meet power efficiency and memory constraints.

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

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

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

• Programme Arduinos to control lights, motors, and other devices.
• Comprehend 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 enhance Arduino's functionality.
• Appreciate the range of programming language options available, from C to drag-and-drop interfaces.
• Test, debug, and deploy Arduino solutions to address real-world challenges.

The RISC-V ecosystem has evolved from a specialized open-source instruction set architecture (ISA) into a dominant mainstream architecture, gaining substantial traction in 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 the semiconductor industry anticipates over 15,000 open roles. Current hiring trends indicate that employers prioritise candidates with proficiency in RISC-V architecture alongside skills in System-on-Chip (SoC) design, RTL verification using UVM and SystemVerilog, AI accelerator development, systems programming in Rust, confidential computing, and open-source toolchain expertise. The most rapidly growing competencies include automotive-grade RISC-V compliant with ISO 26262, server-class processors featuring AIA interrupt controllers and multi-core coherence, and edge AI inference NPUs. Leading companies such as SiFive, Qualcomm, and Western Digital have expedited their RISC-V initiatives, intensifying the demand for engineers capable of integrating architecture specification, silicon implementation, firmware, and software stack development within a unified skill set.