Online Microcontroller Training in US

This instructor-led, live training (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who are interested in deploying machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse for government applications. 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 suitable for TinyML projects. - Train, optimize, and deploy AI models on low-power microcontrollers. - Utilize TensorFlow Lite and Edge Impulse to implement practical TinyML applications. - Optimize AI models to meet power efficiency and memory constraints.

This instructor-led, live training in US (online or onsite) is designed for engineers who seek to acquire the skills necessary to utilize embedded C for programming a variety of microcontrollers based on different processor architectures, including 8051, ARM Cortex-M3, and ARM9. The training aligns with professional development goals for government and industry professionals, ensuring participants gain practical knowledge that can be directly applied to enhance technical capabilities in their respective fields.

In this instructor-led, live training for government participants, attendees will learn how to program the Arduino for practical applications, such as controlling lights, motors, and motion detection sensors. This course assumes the use of actual 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 various devices, including lights and motors. - Understand Arduino’s architecture, including inputs and connectors for add-on devices. - Integrate third-party components such as LCDs, accelerometers, gyroscopes, and GPS trackers to enhance Arduino's capabilities. - Explore the different programming language options available, from C to drag-and-drop languages. - Test, debug, and deploy Arduino solutions to address real-world challenges.

The RISC-V architecture has evolved from a specialized open-source instruction set interface into a dominant platform with expanding application across edge computing, the Internet of Things (IoT), automotive systems, artificial intelligence acceleration, and server infrastructure. Current workforce assessments highlight a substantial gap between available talent and industry demand, with fewer than 5,000 qualified designers globally competing for more than 15,000 open positions within the semiconductor sector. Federal and commercial entities seeking to align with public sector needs must prioritize candidates who demonstrate proficiency in RISC-V architecture alongside System-on-Chip (SoC) design, Register Transfer Level (RTL) verification using UVM or SystemVerilog, and development of AI accelerators. Essential technical competencies also include systems programming in Rust, implementation of confidential computing protocols, and mastery of open-source toolchains. Rapid growth is evident in automotive-grade applications adhering to ISO 26262 standards, server-class processing with advanced interrupt controllers and multi-core coherence, and neural processing units for edge AI inference. Industry leaders such as SiFive, Qualcomm, and Western Digital are intensifying development efforts, thereby increasing the requirement for engineers capable of integrating architecture specification, silicon implementation, firmware, and software stack development into a unified operational framework for government and critical infrastructure projects.