NPU, GPU, and TPU Architectural Comparison: Inputs and Outputs

Inputs and outputs contract

Inputs and Outputs 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.

INPUTS
  - workload profile and SLA target
  - model precision and quality thresholds
  - compiler/runtime/firmware metadata
  - hardware operating envelope assumptions

OUTPUTS
  - evidence-backed bottleneck classification
  - owner-signed mitigation proposal
  - validation matrix with rollback triggers
  - release recommendation

Ownership split

OWNERSHIP LAYERS - NPU, GPU, and TPU Architectural Comparison

+----------------------+--------------------------------+--------------------------------+
| Team                 | Primary responsibility         | Closure artifact               |
+----------------------+--------------------------------+--------------------------------+
| AI systems architect | mechanism and architecture intent| design rationale + tradeoffs   |
| accelerator microarchitecture lead | mapping, runtime, and execution   | profile traces + bottleneck map|
| compiler and runtime owner | correctness, risk, and signoff    | test report + closure memo     |
+----------------------+--------------------------------+--------------------------------+

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.

Handoff explanation

Inputs should include workload profile, model revision, compiler/runtime versions, and platform power mode.

Outputs must include actionable interpretation of Tokens or inferences per second per watt at target model quality and batch profile., required artifacts (Decision matrix mapping operator mix, precision needs, and deployment constraints to accelerator class.), owner, and validation scope.

The ideal handoff packet is reproducible: fixed seeds, explicit baseline, and rejected alternatives.