Advanced Embedded Systems Development Training Course

In this instructor-led, live training held in US, participants will acquire the skills necessary to program Arduino for real-world operational tasks, including the control of lighting, motor systems, and motion detection sensors. This course requires the utilization of physical hardware components in a live laboratory setting (excluding software-simulated environments).

By the conclusion of this training, participants will be capable of:

• Programming Arduino to manage lighting, motors, and auxiliary devices.
• Comprehending Arduino architecture, specifically input and connector interfaces for modular add-on devices.
• Integrating third-party components, including LCD displays, accelerometers, gyroscopes, and GPS trackers, to extend system functionality.
• Understanding various programming language options, from C-based code to visual drag-and-drop tools.
• Testing, debugging, and deploying Arduino systems to resolve practical operational issues.

This instructor-led, live training (online or onsite) is aimed at C developers wishing to learn embedded C design principles for government.

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

• Understand the design considerations that make embedded C programs reliable
• Define the functionality of an embedded system
• Define the program logic and structure to obtain the desired result
• Design a reliable, error-free embedded application
• Obtain optimal performance from target hardware

Format of the Course:

• Interactive lecture and discussion
• Exercises and practice
• Hands-on implementation in a live-lab environment

Course Customization Options:

• To request a customized training for this course, please contact us to arrange.

This instructor-led live training in US (available online or on-site) targets intermediate-level embedded systems engineers and AI developers aiming to implement machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse for government operations.

Upon completion of this training, participants will demonstrate the ability to:

• Explain the core principles of TinyML and its operational advantages in edge AI contexts.
• Configure and manage a development environment suitable for TinyML initiatives.
• Train, refine, and implement AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to develop practical TinyML solutions for government use cases.
• Enhance AI model performance regarding power efficiency and memory limitations.

The RISC-V ecosystem has evolved from a specialized open-source instruction set architecture to a mainstream platform demonstrating significant market momentum across edge computing, IoT, automotive, AI acceleration, and server-class processor domains. Industry analyses indicate a critical talent shortage: fewer than 5,000 RISC-V chip designers are currently active globally, contrasted against an estimated 15,000+ open positions within the semiconductor sector. Key hiring trends reveal that employers prioritize RISC-V architecture proficiency combined with SoC design, RTL verification (UVM/SystemVerilog), AI accelerator development, Rust systems programming, confidential computing, and open-source toolchain expertise. The expansion of automotive-grade RISC-V implementations (ISO 26262), server-class processors (AIA interrupt controllers, multi-core coherence), and edge AI inference NPUs represents the fastest-growing competency areas. Organizations including SiFive, Qualcomm, and Western Digital have accelerated RISC-V development, driving demand for engineers capable of bridging architecture specification, silicon implementation, firmware, and software stack development within a unified skill set for government and public sector applications.