RELAX: a REconfigurabLe Approximate Network-on-Chip
Bibliographic record
Abstract
The high error-resilience of numerous applications such as neural networks and signal processing led to new optimization opportunities in manycore systems. Indeed, approximate computing enable the reduction of data bit size, which allows to relax design constraints of computing resources and memory. However, on-chip interconnects can hardly take advantage of the reduced data size since they also need to transmit plain sized data. Consequently, existing approximate networks-on-chip (NoCs) either involve additional physical layers dedicated to approximate data or significantly increase the energy to transfer non-approximate data. To solve this challenge, we propose RELAX, a reconfigurable network-on-chip that can operate in an accurate data only mode or a mixed mode. The mixed mode allows for concurrent accurate and approximate data transactions using the same physical layer, hence allowing the efficient transmission of approximate data while reducing the resources overhead. Synthesis and simulation results show that RELAX improves communication latency of approximate data up to 44.2% when compared to an accurate data only, baseline 2D-Mesh NoC.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".