Low-power system-level design of VLSI packet switching fabrics
Bibliographic record
Abstract
System-level design of packet switching fabrics focuses on performance metrics and rarely considers the physical requirements that are usually addressed later at the circuit-level. However, low-power dissipation has become a major requirement in such fabrics dictated by the requirements of emerging applications and by the recent advances in fabrication and VLSI technologies. This paper proposes a framework for system-level design of packet switching fabrics that integrates performance specifications along with physical requirements and constraints. Moreover, realistic traffic models are used to derive the transition activity and the packet arrival and departure events needed for power estimation. Physical requirements are defined by an architectural model for power dissipation based on the stochastic traffic model, models for silicon area, chip count, and input-output pins, which provide a complete system-level specification of the fabric. Performance constraints are also derived from the stochastic traffic model. This framework formulates and solves the power optimization problem subject to those physical and performance constraints as an integer nonlinear optimization problem. The results obtained emphasize the importance of traffic-driven system-level optimization and show the efficiency of this framework as a system-level design space exploration tool.
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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) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".