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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".