TPU (Tensor Processing Unit)

Definition

TPUs are Google's custom-designed chips optimized specifically for machine learning.

Versions: - TPU v1: Inference only - TPU v2/v3: Training capable - TPU v4: Current generation - TPU v5: Latest, most powerful

Architecture: - Systolic array for matrix multiplication - High-bandwidth memory - Designed for TensorFlow/JAX - Connected in pods for scale

Advantages: - Optimized for ML workloads - Cost-effective at scale - High throughput - Used for Google's own training

Access: - Google Cloud (TPU VMs) - Colab (free TPU access) - Google Research programs

Vs GPUs: - TPUs: Better for large-scale training - GPUs: More flexible, wider ecosystem

Google trained PaLM and Gemini on TPU v4 pods.

The process of teaching an AI model to perform tasks by exposing it to data.

Specialized processor essential for training and running AI models efficiently.