AI for HW with GitHub Copilot

Controlled changes. Reviewable evidence.

A practical course for hardware, embedded-software, validation, and verification engineers. Follow one engineering workflow across six recorded lessons: define the task, control the change, test the requirement, and hand off evidence another engineer can reproduce.

Six recorded lessons and one 20-question multiple-choice knowledge check. All six lesson videos are available now.

Free and open access. Watch all six videos and read the lesson transcripts without signing in. A free account is required to submit the final quiz and save your results.

Who this course is for

Hardware engineers; embedded-software developers; validation and verification engineers; technical leads working with hardware-related code, test logs, and engineering tools.

Learning outcomes

• Distinguish an explanation, a proposed change, and an executed action; apply approved permissions and review controls.
• Translate an approved specification into a traceable task with units, constraints, open questions, and acceptance criteria.
• Review a bounded code change for signedness, units, error handling, interfaces, and unintended edits.
• Investigate log discrepancies with a minimal reproducer and evidence-based debugging.
• Derive tests from requirements and use a meaningful Red-Green-Refactor cycle.
• Package code, tests, configuration, operating instructions, and validation limits for reproducible peer handoff.

Prerequisites and setup

• Basic programming experience and familiarity with hardware development or verification.
• Basic Git skills: inspect a diff, identify a revision, and review a small change.
• Organization-approved access to GitHub Copilot and an approved editor, toolchain, and development environment for hands-on work.
• Use approved, non-sensitive training data. Access to licensed simulators or live hardware is not assumed.

The six-lesson workflow

1. GitHub Copilot: Engineering Workflow and Control
   Set up a controlled AI-assisted engineering workflow before accepting any code change.
2. From Hardware Specifications to an Executable Task
   Turn an approved engineering source into a precise implementation request with independently checked expectations.
3. Generate, Review, and Run a Bounded Code Change
   Implement a small, authorized change while preserving the public interface and collecting real execution evidence.
4. Log Analysis and Evidence-Based Debugging
   Find the boundary where valid engineering meaning is lost instead of treating every report discrepancy as a hardware fault.
5. Test the Requirement, Not Just the Implementation
   Use engineering requirements as the test oracle and make each test support a specific, reviewable claim.
6. From a Working Change to a Reproducible Engineering Deliverable
   Make the result reproducible by another engineer and keep the validation boundary explicit.

Final knowledge check

20 multiple-choice questions, one correct answer per question, with explanations after submission. Passing score: 80% (16 of 20). Three attempts. No time limit. This quiz checks engineering judgment; it is not a certification of practical hardware competence.

Scope

The worked examples use an offline regression-log utility and a fictional sensor-decoding and reporting pipeline. Python or Tcl, C/C++, RTL, testbenches, and UVM are alternative application tracks, not a promise to teach every language or methodology in this course. Host-side or simulated checks do not replace validation on the target hardware.

GitHub Copilot features and tool permissions depend on the approved product, editor, plan, and organization settings. This is an ArtificialGate course; no GitHub or Microsoft endorsement is implied.

Tool reference

GitHub Docs: using Copilot in your IDE