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submission_checklist.md

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MLPerf Inference 0.7 Self-Certification Checklist

Name of Certifying Engineer(s):

Email of Certifying Engineer(s):

Name of System(s) Under Test:

Does the submission run the same code in accuracy and performance modes? (check one)

  • Yes
  • No

Where is the LoadGen trace stored? (check one)

  • Host DRAM
  • Other, please specify:

Are the weights calibrated using data outside of the calibration set? (check one)

  • Yes
  • No

What untimed pre-processing does the submission use? (check all that apply)

  • Resize
  • Reorder channels or transpose
  • Pad
  • A single crop
  • Mean subtraction and normalization
  • Convert to whitelisted format
  • No pre-processing
  • Other, please specify:

What numerics does the submission use? (check all that apply)

  • INT4
  • INT8
  • INT16
  • INT32
  • UINT8
  • UINT16
  • UINT32
  • FP11
  • FP16
  • BF16
  • FP32
  • Other, please specify:

Which of the following techniques does the submission use? (check all that apply)

  • Wholesale weight replacement
  • Weight supplements
  • Discarding non-zero weight elements
  • Pruning
  • Caching queries
  • Caching responses
  • Caching intermediate computations
  • Modifying weights during the timed portion of an inference run
  • Weight quantization algorithms that are similar in size to the non-zero weights they produce
  • Hard coding the total number of queries
  • Techniques that boost performance for fixed length experiments but are inapplicable to long-running services except in the offline scenario
  • Using knowledge of the LoadGen implementation to predict upcoming lulls or spikes in the server scenario
  • Treating beams in a beam search differently. For example, employing different precision for different beams
  • Changing the number of beams per beam search relative to the reference
  • Incorporating explicit statistical information about the performance or accuracy sets
  • Techniques that take advantage of upsampled images.
  • Techniques that only improve performance when there are identical samples in a query.
  • None of the above

Is the submission congruent with all relevant MLPerf rules?

  • Yes
  • No

For each SUT, does the submission accurately reflect the real-world performance of the SUT?

  • Yes
  • No