Microcontroller Training in Banat

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

Upon completing this training, participants will be able to: <\/p>

• Comprehend the core principles of TinyML and its advantages for edge AI applications. <\/li>
• Configure a development environment tailored for TinyML projects. <\/li>
• Train, optimize, and deploy AI models on low-power microcontrollers. <\/li>
• Leverage TensorFlow Lite and Edge Impulse to create real-world TinyML solutions. <\/li>
• Optimize AI models to meet power efficiency requirements and memory limitations. <\/li> <\/ul>

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

In this instructor-led live training in Banat, 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 real hardware components in a live lab environment, rather than software-simulated hardware.

Upon completing 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 adding peripherals.
• Integrate third-party components like LCDs, accelerometers, gyroscopes, and GPS trackers to expand Arduino's capabilities.
• Explore various programming language options, ranging 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 niche open-source instruction set architecture (ISA) into a mainstream architectural choice, gaining significant market traction across edge computing, IoT, automotive systems, AI acceleration, and server-grade processors. Industry analyses highlight a pressing talent deficit: with fewer than 5,000 RISC-V chip designers worldwide compared to an estimated 15,000+ open roles in the semiconductor sector. Current hiring trends indicate that employers are seeking candidates who combine proficiency in RISC-V architecture with skills in 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. Organizations such as SiFive, Qualcomm, and Western Digital are accelerating their RISC-V initiatives, thereby increasing the demand for engineers capable of integrating architecture specification, silicon implementation, firmware, and software stack development within a unified skill set.