Hazards and Forwarding Networks — Step-by-Step Walkthrough

Step-by-step analysis walkthrough

Follow this walkthrough when you own Hazards and Forwarding Networks in a performance review or architecture signoff meeting.

1. Confirm workload and analysis tag.

2. Open Hazard stall + forwarding correctness dashboard and capture worst cluster.

3. Classify bottleneck type.

4. Map cluster to structure.

5. List competing hypotheses.

6. Run cheapest falsifying experiment.

7. Estimate metric delta.

8. Choose bounded change.

9. List regression surfaces.

10. Replay workloads.

11. Write decision memo.

12. Capture methodology guardrail.

Artifacts to collect

• Workload list

• PMU/trace config

• Metric dashboard

• Decision memo

Decision memo template

DECISION MEMO — Hazards and Forwarding Networks
metric:
hypothesis:
experiment:
decision:
validation:

Architecture deep dive

Pipeline depth and width are bets on branch predictability and cache behavior.

Concept diagram

PIPELINE VIEW

Fetch ──► Decode ──► Rename ──► Issue ──► Execute ──► Memory ──► Commit
  │         │          │          │          │          │          │
  ▼         ▼          ▼          ▼          ▼          ▼          ▼
I-cache   decode     ROB/RS     wakeup     ALU/BR     LSU       retire
miss      bubbles    full       select     latency    miss      bandwidth

Every pipeline discussion should name where bubbles enter and where they retire.

Metric graph

STALL STACK EXAMPLE

cycles (%)
frontend       ██████████████  28
branch         ████████        16
backend        ████████████    24
memory         █████████       18
retire/other   ██████          12

Read this before saying "make the pipe wider."

Metrics and artifacts

• IPC/CPI breakdown

• stall cycles by stage

• branch mispredict rate

• frontend vs backend bound

Mini case study

IPC drops after widening decode but branch-heavy workload shows frontend stalls unchanged. The correct read: backend was not the bottleneck — branch prediction and fetch bandwidth need investment first.

Debug branches

• If IPC flat after deeper pipeline, check branch MPKI and cache miss stalls.

• If hold timing fails on critical path, architecture may need shorter pipeline stage — link PD.

Senior review question

Ask: what single metric would prove this concept is working or failing on your workload?

Key takeaways

• Connect every architecture claim to a workload and measurable metric.

• State verification and PPA impact before proposing design changes.

Common pitfalls

• Feature-driven design without MPKI/IPC/bandwidth evidence.

• Ignoring coherency and NoC traffic in cache and accelerator sizing.

Study notes

Re-read this topic with one concrete workload.