FPGA × AI Training

Flagship course: FPGA × AI

AI tools are already writing HDL inside your competitors’ design flows. The difference between engineers who gain a real schedule advantage and engineers who ship AI-generated bugs is training. This training teaches participants to use LLMs and agentic AI across the entire FPGA design flow — and, just as importantly, to know exactly when not to trust them.

• Prompt engineering for RTL. Getting correct, synthesizable HDL out of AI tools — and recognizing the failure modes that look correct but aren’t.
• AI-assisted design & feedback loops. Wiring AI into simulation, synthesis, and lint so it iterates against real tool feedback instead of guessing.
• From Python model to bitstream. A complete worked flow: algorithm model, RTL implementation, verification, and hardware bring-up with AI assistance at each stage.
• Testbench & debugging automation. Where AI genuinely accelerates verification — testbench generation, log triage, waveform-level debugging assistance.
• Agent loops. Beyond single prompts: setting up agentic workflows where the AI plans, edits RTL, runs the tools, reads the results, and iterates until the design passes — with the guardrails that keep it honest.
• Skills & hooks. Encoding your design rules, coding standards, and tool flows as reusable AI skills and automated hooks, so every AI-assisted change follows your process — not the model’s habits.
• Hardware in the loop. Closing the loop on real silicon: driving boards, capturing live debug data, and feeding lab results back to the AI so bring-up issues get diagnosed against actual hardware behavior, not simulation alone.
• Hands-on exercises throughout. Every participant works on real tools with real designs. No slideware-only sessions.
• AI-generated HDL review checklist. A take-home discipline you can apply to every AI-assisted change after the course ends.

Suitable for FPGA engineers at different experience levels: newcomers to AI leave with a structured workflow, while experienced users gain the review discipline to supervise AI output safely.

What the training covers

The course follows the complete AI-assisted FPGA workflow and is designed for engineers working in Verilog, SystemVerilog, or VHDL.

• Vendor-independent methods. Apply the workflow across AMD/Xilinx Vivado, Intel/Altera Quartus, Lattice, Microchip, and Efinix tools.
• Practical verification. Use simulation, lint, synthesis, timing reports, and hardware feedback to validate every AI-generated change.
• Reusable workflows. Build prompts, skills, hooks, review checklists, and agent loops that fit real FPGA development.
• Real hardware thinking. Work from algorithm and RTL through debugging, implementation, and board bring-up.

Approximately 16 hours of focused material and hands-on exercises, with delivery adapted to the selected format.

Why train with bard0

• Practitioners, not career trainers. Your instructor spends the rest of the week designing FPGA systems for clients in embedded vision, aerospace, and medical imaging. The material is what we actually do.
• We use AI-assisted design daily. Our own flow is built on it — including our open-source tooling that connects AI assistants to real FPGA toolchains. We teach from production experience, not from a demo.
• Hands-on by default. Every module has exercises on real tools. Your engineers leave with working flows, not certificates.
• Flexible delivery. Join online as an individual, train a remote team, or arrange tailored corporate and onsite sessions.

Frequently asked questions

Who is the FPGA × AI course for?

FPGA and embedded engineers who want to use AI tools productively and safely. Engineers newer to AI gain a structured workflow, while experienced users gain stronger review discipline.

How is the course delivered?

Training is available online for individuals and teams, with corporate and onsite delivery available by arrangement. Every format includes practical exercises using real FPGA tools.

Which FPGA toolchains are covered?

The methods apply across AMD/Xilinx Vivado, Intel/Altera Quartus, Lattice, Microchip, and Efinix flows, and are relevant to Verilog, SystemVerilog, and VHDL users.

Do AI tools actually help with FPGA design?

Used correctly, yes: RTL prototyping, testbench generation, debug triage, and code review are proven wins. Used naively, they produce plausible-looking HDL that fails in synthesis or in the lab. The course teaches where that line is—see our practical guide to Vivado’s AI chatbot for a taste of the approach.

How long is the course?

The core training contains approximately 16 hours of focused material and hands-on exercises; scheduling can be adapted to the delivery format.