Agentic AI FPGA Training

Flagship course "FPGA × AI": agentic, AI-assisted design

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.
• Token & cost efficiency. Keeping AI spend under control — right-sizing context, picking the right model for each task, caching, and designing agent loops that converge quickly instead of burning tokens in circles. The same result for a fraction of the tokens, so cost stays flat as AI use scales across a team.
• 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 and digital design 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.
• Token and cost efficiency. Minimize token usage and keep AI tooling affordable as adoption scales across a team — a growing concern for engineering budgets.
• Real hardware thinking. Work from algorithm and RTL through debugging, implementation, and board bring-up.

New to FPGA? Start with the primer

For engineers or teams without an FPGA or digital-design background, we can prepend an optional Introduction to Digital Design & FPGAs primer, so everyone starts the AI-assisted material on solid ground. It is hands-on and paced for newcomers, and by the end the team is ready for the full FPGA × AI course.

• Digital logic & RTL foundations. Combinational and sequential logic, registers, and clocking — and how RTL describes hardware, not software.
• The FPGA design flow, end to end. From HDL through simulation, synthesis, place-and-route, and bitstream — and what each stage is actually doing.
• Simulation, synthesis & timing basics. Reading tool output, writing a first testbench, and what a timing report is telling you.
• Ready for FPGA × AI. Enough grounding to supervise AI-assisted RTL, not just accept it.

Just tell us your team’s background and we’ll scope the right starting point.

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 build with agentic AI, not just teach it. Three of our open-source FPGA projects — mjpegZero (an MJPEG/JPEG encoder), emacZero (an Ethernet MAC), and fpgacapZero (fcapz, an FPGA debug/capture tool) — were designed end-to-end with the same agentic, AI-assisted workflow we teach, and our flow runs on our own open-source MCP tooling that connects AI assistants to real FPGA toolchains. We even publish our own agent skills, like hdldiagZero, which turns an RTL or SoC architecture description into a clean SVG block diagram. We teach from production experience, not from a demo.
• Hands-on by default. Every module has exercises on real tools. 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.

Pair it with local AI

Everything we teach also runs on local, on-premise AI — open-weight models served inside your network, so RTL, schematics, and design data never leave the building. If NDAs, export control, or defense and medical compliance rules keep your team away from cloud AI tools, we set up the private infrastructure as a separate service and deliver the training on it, so your engineers learn on the exact stack they’ll use every day.

Local AI setup for engineering teams →

Frequently asked questions

Who is the FPGA × AI course for?

FPGA, digital design, 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.

What if my engineers don’t have FPGA or digital design experience?

No problem. For individuals or teams without a hardware background, we can prepend an optional Introduction to Digital Design & FPGAs primer—covering digital logic, RTL, and the FPGA design flow—so everyone is ready for the AI-assisted material. Mention your team’s background when you get in touch and we’ll scope the right starting point.

Is this agentic AI or AI-copilot training for FPGA engineers?

Yes. The course covers using LLMs, AI copilots, and agentic AI workflows—including MCP-based tooling that connects AI assistants to real FPGA toolchains—across RTL design, verification, and debug. The methods work with today’s leading assistants—Claude and Claude Code, GitHub Copilot, Cursor, OpenAI Codex, ChatGPT and Gemini—and stay vendor- and tool-independent, so they carry across whichever assistant you adopt next.

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 design, architecture exploration, testbench/verification generation, 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.

Can you set up local AI or on-premise LLMs for our team?

Yes, as a separate service that pairs naturally with the training: self-hosted open-weight models, private endpoints for agentic coding tools, and retrieval over internal documentation, including air-gapped environments. See the dedicated local AI setup page for what’s included.

How long is the course?

The core training can be delivered in 1 or 2 days format of focused material plus hands-on training exercises; scheduling can be adapted to the delivery format.