Bibliographic record
Abstract
Chip designers have shown increasing interest in integrating specialized fixed-function coprocessors into multicore designs to improve energy efficiency. Recent work in academia [11, 37] and industry [16] has sought to enable more fine-grain offloading at the granularity of functions and loops. The sequential program now needs to migrate across the chip utilizing the appropriate accelerator for each program region. As the execution migrates, it has become increasingly challenging to retain the temporal and spatial locality of the original program as well as manage the data sharing. We show that with the increasing energy cost of wires and caches relative to compute operations, it is imperative to optimize data movement to retain the energy benefits of accelerators. We develop FUSION, a lightweight coherent cache hierarchy for accelerators and study the tradeoffs compared to a scratchpad based architecture. We find that coherency, both between the accelerators and with the CPU, can help minimize data movement and save energy. FUSION leverages temporal coherence [32] to optimize data movement within the accelerator tile. The accelerator tile includes small per-accelerator L0 caches to minimize hit energy and a per-tile shared cache to improve localized-sharing between accelerators and minimize data exchanges with the host LLC. We find that overall FUSION improves performance by 4.3× compared to an oracle DMA that pushes data into the scratchpad. In workloads with inter-accelerator sharing we save up to 10x the dynamic energy of the cache hierarchy by minimizing the host-accelerator data ping-ponging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.090 | 0.040 |
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".