Color permutation: an iterative algorithm for memory packing
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
It is predicted that 70% of silicon real-estate will be occupied by memories in future system-on-chips. The minimization of on-chip memory hence becomes increasingly important for cost, performance and energy consumption. In this paper, we present a reasonably fast algorithm based on iterative improvement, which packs a large number of memory blocks into a minimum-size address space. The efficiency of the algorithm is achieved by two new techniques. First, in order to evaluate each solution in linear time, we propose a new algorithm based on the acyclic orientation of the memory conflict graph. Second, we propose a novel representation of the solution which effectively compresses the potentially infinite solution space to a finite value of n!, where n is the number of vertices in the memory conflict graph. Furthermore, if a near-optimal solution is satisfactory, this value can be dramatically reduced to /spl chi/!, where /spl chi/! is the chromatic number of the memory conflict graph. Experiments show that consistent improvement over scalar method by 30% can be achieved.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it