PIMnast: Balanced Data Placement for GEMV Acceleration with Processing-In-Memory
Bibliographic record
Abstract
With unprecedented demand for generative AI (GenAI) inference, acceleration of primitives that dominate GenAI such as general matrix-vector multiplication (GEMV) is receiving considerable attention. A challenge with GEMVs is the high memory bandwidth this primitive demands. Multiple memory vendors have proposed commercially viable processing-in-memory (PIM) prototypes that attain bandwidth boost over processor via augmenting memory banks with compute capabilities and broadcasting same command to all banks. While proposed PIM designs stand to accelerate GEMV, we observe in this work that a key impediment to truly harness PIM acceleration is deducing optimal data-placement to place the matrix in memory banks. To this end, we tease out several factors that impact data-placement and propose PIMnast methodology which, like a gymnast, balances these factors to identify data-placements that deliver GEMV acceleration. Across a spectrum of GenAI models, our proposed PIMnast methodology along with additional orchestration knobs we identify delivers up to 6.86× speedup for GEMVs (of the available 7 roofline speedup) leading to up to 5× speedup for per-token latencies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".