Lilly Tech Systems
Learn AI Chip Design
Understand the specialized hardware that powers modern AI. From Neural Processing Units and custom ASICs to FPGA accelerators — learn how silicon is designed and optimized for the massive parallel computation that AI workloads demand.
Your Learning Path
Follow these lessons to understand the hardware foundations of AI computing.
1. Introduction
Why AI needs specialized hardware. CPU limitations, parallelism, and the rise of AI accelerators.
2. NPU Architecture
Neural Processing Unit design. Systolic arrays, matrix engines, on-chip memory, and dataflow architectures.
3. ASIC Design
Custom AI chips: Google TPU, AWS Trainium, Tesla Dojo, and the ASIC design process for AI workloads.
4. FPGA for AI
Reconfigurable hardware for AI inference. FPGA advantages, design tools, and deployment strategies.
5. Comparison
GPU vs NPU vs ASIC vs FPGA. Performance, cost, power efficiency, and choosing the right accelerator.
6. Best Practices
Hardware selection, optimization techniques, future trends, and building hardware-aware AI systems.
What You'll Learn
By the end of this course, you'll be able to:
Understand NPU Design
Know how neural processing units accelerate AI inference with specialized architectures.
Evaluate AI ASICs
Understand the design trade-offs behind Google TPU, AWS Trainium, and other custom AI chips.
Use FPGA for AI
Learn when and how to use reconfigurable hardware for AI inference acceleration.
Choose the Right Hardware
Make informed decisions about which AI accelerator fits your workload and budget.