Evaluating address register assignment and offset assignment algorithms
Bibliographic record
Abstract
In digital signal processors (DSPs), variables are accessed using k address registers. The problem of finding a memory layout, for a set of variables, that minimizes the address-computation overhead is known as the General Offset Assignment (GOA) problem. The most common approach to this problem is to partition the set of variables into k partitions and to assign each partition to an address register. Thus, effectively decomposing the GOA problem into several Simple Offset Assignment (SOA) problems. Many heuristic-based algorithms are proposed in the literature to approximate solutions to both the variable partitioning and the SOA problems. However, the address-computation overhead of the resulting memory layouts are not accurately evaluated. This article presents an evaluation of memory layouts that uses Gebotys' optimal address-code generation technique. The use of this evaluation method leads to a new optimization problem: the Memory Layout Permutation (MLP) problem. We then use Gebotys' technique and an exhaustive solution to the MLP problem to evaluate heuristic-based offset-assignment algorithms. The memory layouts produced by each algorithm are compared against each other and against the optimal layouts. The results show that even in small access sequences with 12 variables or less, current heuristics may produce memory layouts with address-computation overheads up to two times higher than the overhead of an optimal layout.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".