Training & Timing Modes: Reports & Metrics

Reports and metrics

Reports & Metrics for Training & Timing Modes focuses on training margin, eye width, boot failure rate. The goal is to connect the observable symptom to protocol mechanism, ownership, and regression risk.

The job of a report is to turn training margin, eye width, boot failure rate into a decision. A single average number is almost never enough; you need the distribution, the traffic class breakdown, and a clear gap between legal maximum and product target.

Metric movement

METRIC GRAPH — training margin, eye width, boot failure rate

throughput / success
  ^
  |                         target
  |                       - - - - - - -
  |                  o after bounded fix
  |              o
  |         o baseline
  |    o failing run
  +--------------------------------------> experiment
    config A     isolated root cause     accepted change

Readout:
  - compare identical payload, clock, reset, traffic seed, and firmware setup
  - separate headline bandwidth from useful payload bandwidth
  - explain why the protocol mechanism moved the metric

Latency distribution

LATENCY HISTOGRAM — Training & Timing Modes

count
  |               ███
  |             ███████
  |          █████████████
  |        █████████████████        <- long tail = the real complaint
  |      ████████████████████████████
  +------------------------------------> latency
   p50      p90    p95       p99  (watch p99, not the average)

Average hides the tail; product pain lives at p95/p99.

• Track training margin, eye width, boot failure rate by traffic class, payload size, and clock/reset mode.

• Report p50/p95/p99 latency when user-visible stalls matter.

• Include legal maximums and product targets; they are not the same thing.

• Always store the metric next to the artifact that produced it.

Protocol deep dive

DDR bandwidth is scheduler + PHY: rows, banks, refresh, and turnarounds eat headline data rate.

Concept diagram

MEMORY PATH

masters -> controller scheduler -> PHY -> DRAM banks
              |                      |
         refresh/QoS            training/margin

Scheduler sees transactions; PHY sees picoseconds.

Metric graph

BANDWIDTH LOSS WATERFALL

peak              ████████████████████████
refresh           █████████████████████
turnaround        ██████████████████
row miss          ██████████████
effective         ██████████████

Quote the bottom bar in reviews.

Metrics and artifacts to collect

• effective BW

• row hit rate

• refresh stall %

• training margin

• ECC error log

Mini case study

Video workload lost half effective bandwidth after firmware enabled aggressive low-power refresh. Scheduler and firmware QoS had to be co-designed.

Debug branches

• If ECC errors, check training margin and address interleave first.

• If BW low with high row hit, suspect port arbitration not DRAM.

• If boot fail, stop at training step in transcript.

Senior review question

Ask: what is the first transaction that deviates, and which spec rule does it test?

Key takeaways

• Connect every protocol claim to a transaction identity and measurable metric.

• Store the artifact (waveform, log, counter) next to every signoff decision.

Common pitfalls

• Debugging timeouts without finding the first bad transaction.

• Quoting peak bus width without payload efficiency and retry overhead.

• Treating VIP compliance as a substitute for system integration replay.

How to read the numbers

training margin, eye width, boot failure rate must be split by traffic class, payload size, and reset mode.