Courses & Programs

Core Foundations

Foundational modules connecting ML concepts with real hardware constraints: memory, dataflow, and parallelism.

ML-to-FPGA Essentials

Your first practical steps into ML-FPGA integration. Learn how a neural network becomes hardware and how compute blocks, DSPs, and on-chip memories shape performance.

Full Pipeline Integration

End-to-end workflow: training → optimization → IP core → firmware → deployment. Ideal for teams aiming to understand the complete ML-hardware path.

Advanced Optimization & Acceleration

Deep dive into performance-critical techniques: quantization, pruning, distillation, fusion, profiling, and hardware-aware benchmarking.

Hands-On FPGA Lab (Cohort)

A fully guided, practice-first program. Real FPGA boards, experiments, debugging sessions, and direct mentoring.

Full Pipeline (2 Weeks)

Learn the full ML-to-hardware workflow. FPGA is the final deployment target, not the main learning tool.

• Training → optimization → IP core → firmware → deployment
• Structured, reproducible examples
• Methodology-focused
• FPGA: light usage (demo/deployment)

Guided Lab (2 Weeks)

A fully hands-on FPGA experience. The board is the primary learning environment.

• Board experiments & mini-projects
• Hardware debugging & troubleshooting
• Practice-first learning
• FPGA: heavy usage (daily experiments)