AI Chip
A semiconductor designed specifically for artificial intelligence workloads, optimized for the mathematical operations (matrix multiplication, convolution) that neural networks require.
Why It Matters
AI chips are the foundation of all AI computation. The race to build better, faster, and more efficient AI chips is one of the most important technology competitions.
Example
NVIDIA's H100, Google's TPU v5, Apple's Neural Engine, and Intel's Gaudi — each designed to accelerate different aspects of AI computation.
Think of it like...
Like custom-built racing engines versus general car engines — AI chips sacrifice versatility for extreme performance on the specific operations AI needs.
Related Terms
GPU
Graphics Processing Unit — originally designed for rendering graphics, GPUs excel at the parallel mathematical operations needed for training and running AI models. They are the primary hardware for modern AI.
TPU
Tensor Processing Unit — Google's custom-designed chip specifically optimized for machine learning workloads. TPUs are designed for matrix operations that are fundamental to neural network computation.
ASIC
Application-Specific Integrated Circuit — a chip designed for a single specific purpose. In AI, ASICs like Google's TPUs are designed exclusively for neural network operations.
Hardware Acceleration
Using specialized hardware (GPUs, TPUs, FPGAs, ASICs) to speed up AI computation compared to general-purpose CPUs. Accelerators are optimized for the specific math operations used in neural networks.
Compute
The computational resources (processing power, memory, time) required to train or run AI models. Compute is measured in FLOPs (floating-point operations) and is a primary constraint and cost in AI development.