A novel framework for multi-rate scheduling in DSP applications
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
The authors present a novel framework for multi-rate scheduling of signal processing programs represented by regular stream flow graphs (RSFGs). The nodes of an RSFG may execute at different rates to avoid unbounded storage requirement under repetitive computation. A distinct feature of the scheduling framework, called the multi-rate software pipelining, is to allow maximum overlapping of operations from successive iterations subject only to precedence constraints caused by data dependencies. A novel framework based on linear programming techniques has been proposed to schedule RSFGs. The scheduling problem is formulated as a mathematical problem by capturing data dependencies between two actors as a precedence relation between the firing of these actors. The precedence relationships are represented in the form of a precedence graph. An efficient polynomial-time solution is obtained by observing that the computation rate of the optimal schedule is the minimum cost-to-time ratio cycle (MCTRC) in the precedence graph.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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