AI Accelerator Design · All levels

AI Accelerator Landscape

A practical map of accelerator classes, workload placement, and the throughput-latency-energy tradeoffs that drive product architecture decisions.

Section goal

A practical map of accelerator classes, workload placement, and the throughput-latency-energy tradeoffs that drive product architecture decisions.

How to study this section

Start with each topic hub and restate the mechanism in your own words.
Use reports and debug pages to separate symptoms from root causes.
Practice worked examples and interview drills under fixed metadata.
Close with checklist and silicon impact before making release claims.

Topics

npu-gpu-tpu-compare/ - NPU, GPU, and TPU Architectural Comparison
workload-mapping-basics/ - Workload Mapping Basics: Operators to Hardware
throughput-latency-energy/ - Throughput, Latency, and Energy Tradeoff Analysis
product-fit-decisions/ - Product-Fit Decisions for Edge, Datacenter, and Hybrid

AI accelerator deep dive

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

Concept diagram

diagram

ACCELERATOR LANDSCAPE

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

Metric graph

diagram

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?

Practice this lesson

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