Design Automation Framework for Reconfigurable Interconnection Networks
Bibliographic record
Abstract
A reconfigurable interconnection network (RIN) is a custom-designed on-chip switching network yielding routing solutions for a pre-given set of applications. Like field programmable gate array (FPGA) routing networks, the RIN is used to make reconfigurable interconnections among functional blocks. Unlike FPGAs, the network topology of a RIN is irregular as it is designed for a given set of routing requirements and optimized for the area cost subject to given delay constraints. In this paper, we propose an automatic design scheme for RINs, including routing specification formulation, graph modelings, network topology designs, routing algorithms and multiplexer-based network circuit implementation. The choice of the design scheme is based on the existing routing network design practices and research, which give feasible solutions. Our scheme is to optimize the designs with the choice of design parameters. A computer-aided design (CAD) tool is developed based on the design scheme, which takes a set of routing requirements as input and produces the corresponding RIN network topology and network circuit in hardware description language format. We present the area costs of various RINs generated by the CAD tool subject to delay constraints, and illustrate the RIN design scheme with a reconfigurable multistream video system.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".