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.
Why It Matters
TPUs represent an alternative to GPUs for AI, and Google uses them extensively. The competition between GPUs and TPUs drives hardware innovation.
Example
Google using TPU v5e pods to train Gemini models, with each pod containing thousands of interconnected TPU chips working in parallel.
Think of it like...
Like a purpose-built race car versus a modified street car — TPUs are designed from scratch for one thing (AI computation) and do it extremely efficiently.
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.
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.
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.