Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wire energy has become the major contributor to energy in large lower level caches. While wire energy is related to wire latency its costs are exposed differently in the memory hierarchy. We propose Sub-Level Insertion Policy (SLIP), a cache management policy which improves cache energy consumption by increasing the number of accesses from energy efficient locations while simultaneously decreasing intra-level data movement. In SLIP, each cache level is partitioned into several cache sublevels of differing sizes. Then, the recent reuse distance distribution of a line is used to choose an energy-optimized insertion and movement policy for the line. The policy choice is made by a hardware unit that predicts the number of accesses and inter-level movements. Using a full-system simulation including OS interactions and hardware overheads, we show that SLIP saves 35% energy at the L2 and 22% energy at the L3 level and performs 0.75% better than a regular cache hierarchy in a single core system. When configured to include a bypassing policy, SLIP reduces traffic to DRAM by 2.2%. This is achieved at the cost of storing 12b metadata per cache line (2.3% overhead), a 6b policy in the PTE, and 32b distribution metadata for each page in the DRAM (a overhead of 0.1%). Using SLIP in a multiprogrammed system saves 47% LLC energy, and reduces traffic to DRAM by 5.5%.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| 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 it