Predictor Types and Accuracy: Review Checklist

Review checklist

Review Checklist for Predictor Types and Accuracy centers on global prediction accuracy, MPKI, and confidence calibration error. Tie every claim to a measurable artifact and an owner-controlled action.

• Workload scope and KPI targets are explicit.

• Environment metadata is locked and reproducible.

• Stage-loss classification is backed by evidence.

• Owner and rollback conditions are documented.

• Validation matrix covers perf, correctness, and power.

• Owners signed: branch predictor architect, predictor RTL owner, perf modeling lead.

CPU deep dive

Speculation helps only when wrong-path cost and recovery bandwidth are tightly controlled.

Concept diagram

SPECULATION LOOP

predict direction/target -> speculative fetch/decode -> resolve -> flush/recover

Metric graph

SPECULATION COST MIX

wrong-path decode work  █████
flush recovery delay    ████
refill starvation       ███

Reports and artifacts

• branch accuracy by workload

• BTB/RAS pressure report

• mispredict recovery timeline

• bad-speculation CPI share

Mini case study

Indirect branch aliasing in one service raised wrong-path work enough to dominate total CPI despite high ALU utilization.

Debug branches

• Break down mispredicts by branch family and code region

• Measure flush depth and refill bandwidth separately

• Validate predictor changes under security mitigation settings

Senior review question

Ask: which CPI/latency evidence proves this topic is truly closed beyond synthetic benchmarks?

Key takeaways

• Always connect microarchitectural counter changes to product workload outcomes.

• Lock binary, compiler, firmware, and thermal metadata before comparing CPU traces.

Common pitfalls

• Treating average IPC as sufficient proof while ignoring latency tails and outliers.

• Applying predictor or prefetch tweaks without first-failing-stage attribution.

• Declaring closure without reproducible perf, correctness, and power gates.

Review checklist explanation

A checklist here prevents false confidence. Every item should defend against a known closure failure mode.

For Predictor Types and Accuracy, minimum checklist: workload scope, global prediction accuracy, MPKI, and confidence calibration error, artifact evidence (predictor-type comparison matrix, accuracy-by-workload plot, and confidence histogram), bottleneck class, owner, rollback path, and full-matrix validation.

If architecture or RTL changed, include correctness, power, timing, and observability checks. If software policy changed, include compatibility and deployment safety evidence.