The Basic Architecture of TPU and Analysis of Optimization Scenarios

Over the past decade, transformer models have ballooned from 110 M to 540 B parameters, rendering Central Processing Unit (CPU)/Graphics Processing Unit (GPU) baselines infeasible for both training and latency-critical inference.Google's Tensor Processing Unit (TPU) program-spanning four silicon generations from the 28 nm, 40 W TPU v1 to the 7 nm, 175 W TPU v4has emerged as the first large-scale deployment of domain-specific ASICs purpose-built for dense and semi-structured tensor arithmetic.This paper provides a holistic, quantitative study of the TPU ecosystem.This paper dissects the deterministic, compiler-co-designed micro-architecture of TPU v3, highlighting how systolic arrays, scheduling, and compiler-managed scratchpads eliminate cache-miss variability and sustain 90 %+ MAC utilization.Scenario analyses of four production workloads.This paper further identifies the memory wall, dynamic-shape compilation stalls, and ecosystem breadth as the primary challenges, and map emerging optimizations-weight-streaming, compilerahead sharding via JAX/pjit, optical-circuit switching, and the Pathways runtime-that together promise to extend TPU leadership.