BERTPerf: Inference Latency Predictor for BERT on ARM big.LITTLE Multi-Core Processors
Bibliographic record
Abstract
Hardware-aware Neural Architecture Search (NAS) and mapping & scheduling optimization methods are being used to find efficient implementations of computationally-intense language models such as BERT. This requires measuring real hardware inference latency: good design decisions simply cannot be made with proxy metrics such as FLOPs or the number of parameters. However, the time required to perform on-device latency measurements is prohibitive (e.g., a few days to a few weeks over the course of an optimization run). To address this, we present BERTPerf, a low-cost, highly-accurate method to predict the inference time of BERT on ARM big.LITTLE multi-core processors. BERTPerf exploits latency patterns at the layer-level to reduce on-device latency measurements, and captures the effect of caching and intermediate tensor allocations to reduce latency prediction error. BERTPerf reduces the maximum prediction error by 7–11% compared to the state-of-the-art, and requires 75% less on-device measurements compared to existing work at the same prediction error.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".