TPU (Tensor Processing Unit)

A Tensor Processing Unit is a specialized application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning workloads.

Key Components

• MXU (Matrix Multiply Unit): Specialized for matrix multiplication operations
• VPU (Vector Processing Unit): Handles elementwise operations
• HBM (High Bandwidth Memory): On-chip memory with high throughput

Performance Characteristics

Different TPU versions have varying specifications:

• TPU v5e:

  • ~1.97e14 FLOPs/s (bfloat16)
  • ~8.2e11 bytes/s HBM bandwidth
  • Peak arithmetic intensity: ~240 FLOPs/byte

• TPU v6e:

  • ~9.1e14 FLOPs/s
  • ~1.6TB/s HBM bandwidth

Communication Types

• ICI: Inter-chip interconnect for communication between TPU chips
• DCN: Data center network for communication between TPU pods

Efficiency Considerations

• Compute-bound operations generally require batch sizes >240 tokens for matrix multiplications
• Different operations run on different units (MXU vs. VPU) with different performance characteristics