NPU, GPU, and TPU Architectural Comparison: Worked Example

Worked example

Worked Example for NPU, GPU, and TPU Architectural Comparison is anchored on Tokens or inferences per second per watt at target model quality and batch profile.. Convert measurements into mechanism-backed decisions with clear owner accountability.

A regression appears in Tokens or inferences per second per watt at target model quality and batch profile.. Strong closure isolates first failing stage, proves mechanism, applies one reversible fix, and validates blast radius before release.

Execution lens

ACCELERATOR EXECUTION FLOW - NPU, GPU, and TPU Architectural Comparison

request ingress and model metadata
      |
      v
graph lowering and kernel selection
      |
      v
tile/dataflow scheduling and memory placement
      |
      v
tensor execution + synchronization barriers
      |
      v
result assembly + quality/SLA validation
      |
      v
release decision and rollback guardrails

Decision matrix

EVIDENCE MATRIX - NPU, GPU, and TPU Architectural Comparison

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| Evidence                    | Tells you                      | Does not prove                 | Next action               |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| occupancy + timeline traces | where utilization is lost      | precise root cause             | map to memory and schedule|
| cache/SRAM/bandwidth stats  | data movement pressure         | model-level quality impact     | correlate with quality run|
| counter + profile alignment | bottleneck class confidence    | rollout safety                 | run full regression matrix|
| thermal/power telemetry     | sustained operating envelope   | correctness closure            | pair with verification    |
| before/after scenario pack  | mitigation movement            | long-tail stability            | execute guardrail replay  |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

AI accelerator deep dive

Accelerator selection quality depends on workload realism and full-stack delivery readiness.

Concept diagram

ACCELERATOR LANDSCAPE

model shape + SLA + power budget
  -> candidate platform shortlist
  -> benchmark under production-like load
  -> choose architecture + stack strategy

Metric graph

PLATFORM TRADE CURVE

throughput      ███████████
latency         ███████
energy          ████████
engineering risk █████

Metrics and artifacts to collect

• workload fit matrix

• latency-throughput sweep

• perf-per-watt dashboard

• owner and risk map

Mini case study

A platform looked best on synthetic GEMM but lost in production due to runtime overhead and memory-tail behavior.

Debug branches

• Validate workload representativeness

• Check software-stack maturity

• Tie KPI gains to product SLA

Senior review question

Ask: which first-principles bottleneck class explains the symptom, and what artifact proves it reproducibly?

Key takeaways

• Tie every accelerator claim to a reproducible workload slice and one primary metric trend.

• Prefer bounded fixes with clear owner and rollback boundary over broad tuning bundles.

Common pitfalls

• Optimizing synthetic kernels without production-shape validation.

• Reading average latency while ignoring p95 and p99 behavior.

• Declaring sparse or precision wins without fallback and quality evidence.

Worked-example reasoning

Suppose Tokens or inferences per second per watt at target model quality and batch profile. regresses only under burst traffic. The shallow response is clock scaling. The stronger response is to inspect queueing, mapping, and memory-pressure interactions first.

If occupancy drops with high memory stalls, prioritize locality and scheduling fixes. If occupancy remains high with latency spikes, inspect contention and fairness policy.

Pick one bounded change per hypothesis and validate against baseline artifacts.