Power Analysis of Embedded NoCs on FPGAs and Comparison With Custom Buses
Bibliographic record
Abstract
We propose embedding networks-on-chip (NoCs) on field-programmable gate-arrays (FPGAs) to implement system-level communication. Amongst other benefits, this can alleviate the current challenge of connecting the FPGA's fabric to high-speed I/O and memory interfaces, which are a crucial component of FPGA designs. Our mixed and hard embedded NoCs add only ~1% area to large FPGAs and can run much faster than the core logic, thus keeping up with the speed of I/O and memory interfaces. A detailed power analysis, per NoC component, shows that routers consume 14× less power when implemented hard compared with soft, and whether hard or soft most of the router's power is consumed in the input modules for buffering. For complete systems, hard NoCs consume <;6% (and as low as 3%) of the FPGA's dynamic power budget to support 100 GB/s of communication bandwidth. We find that, depending on design choices, hard NoCs consume 4.5-10.4 mJ of energy per gigabyte of data transferred. Surprisingly, this is comparable with the energy efficiency of the simplest traditional interconnect on an FPGA-soft point-to-point links require 4.7 mJ/GB. When comparing a hard NoC against soft buses that are currently used for interconnection, we find that a typical system is 4× smaller, and uses 23% less energy when implemented using the hard NoC even though it is only 43% utilized.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".