Low-power system-level design of VLSI packet switching fabrics
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
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.
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 | 0.002 | 0.000 |
| 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 | 0.000 | 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