Beginner

Introduction to AI Chip Design

The explosive growth of AI has created an insatiable demand for computing power. General-purpose processors cannot keep up, driving an entire industry of specialized AI chips — from GPUs and TPUs to custom neural processing units.

Why CPUs Are Not Enough

Neural networks consist of millions (or billions) of mathematical operations, primarily matrix multiplications and additions. CPUs are designed for sequential, complex operations — they excel at running diverse software but are fundamentally limited for AI:

  • Sequential execution: CPUs process instructions one at a time per core (with limited parallelism through SIMD). Neural networks need thousands of operations simultaneously
  • Memory bottleneck: Moving data between CPU cache and main memory is slow. AI workloads are data-hungry and memory-bound
  • Overhead: CPUs spend significant transistor area on branch prediction, out-of-order execution, and other features unnecessary for AI math
  • Power inefficiency: General-purpose flexibility comes at a power cost. AI-specific chips do the same math at a fraction of the energy
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Scale of the problem: Training a large language model like GPT-4 is estimated to require 10^25 floating-point operations. On a single modern CPU, this would take thousands of years. With thousands of specialized AI chips working in parallel, it takes weeks to months.

Types of AI Accelerators

TypeExamplesBest For
GPUNVIDIA H100, AMD MI300XTraining large models, general AI workloads
TPU / ASICGoogle TPU v5, AWS TrainiumSpecific workloads at massive scale
NPUApple Neural Engine, Qualcomm HexagonOn-device inference, mobile AI
FPGAIntel Stratix, AMD VersalLow-latency inference, custom precision

Key Concepts

  1. Parallelism

    AI chips achieve speed through massive parallelism. While a CPU might have 8-64 cores, a GPU has thousands of smaller cores, and TPUs use systolic arrays with hundreds of thousands of multiply-accumulate units.

  2. Memory Bandwidth

    AI workloads are often limited by how fast data can be fed to the compute units. High-bandwidth memory (HBM) and on-chip SRAM are critical design considerations.

  3. Precision

    AI computations do not need 64-bit precision. Using FP16, BF16, INT8, or even INT4 arithmetic doubles or quadruples throughput with minimal accuracy loss.

  4. Interconnects

    Training large models requires multiple chips communicating at high speed. NVLink, Infinity Fabric, and custom interconnects determine how well chips scale together.

The AI Chip Landscape

The AI hardware market has exploded with competitors at every level:

  • Data center training: NVIDIA (H100, B200), Google (TPU v5), AMD (MI300X), Intel (Gaudi 3)
  • Data center inference: NVIDIA (L40S), AWS (Inferentia), Groq (LPU), Cerebras (WSE-3)
  • Edge and mobile: Apple (Neural Engine), Qualcomm (Hexagon), Google (Tensor), MediaTek (APU)
  • Startups: Graphcore, SambaNova, Tenstorrent, d-Matrix, and dozens more

What You Will Learn

This course covers the hardware foundations that every AI practitioner should understand:

  • How NPU architectures accelerate neural network operations
  • The ASIC design process for custom AI chips like Google TPU
  • When and how to use FPGAs for AI inference
  • How to compare and choose between different AI accelerators
  • Best practices for hardware-aware AI system design
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No hardware background needed: This course explains AI chip concepts from first principles. You do not need to know digital logic or semiconductor physics to follow along.