Is Error-Free Communication Essential in Data Centers for AI Training?
Bibliographic record
Abstract
Data centers (DCs) are now heavily involved in large-scale artificial intelligence (AI) learning processes. Fiber optic systems are the primary communication medium for enabling DC networks due to their high speed and low energy consumption per bit. Traditionally, fiber optic systems are designed to ensure nearly error-free communication, which increases their power consumption and latency. In this work, we revisit the communication requirements in DCs for AI training by analytically examining how communication channel noise affects the learning process. Our analysis shows that channel errors introduce bounded noise in the model weights, which can be maintained within an acceptable range. Therefore, relaxing the error-free requirement has a negligible impact on AI learning performance while significantly improving power efficiency and latency. Additionally, we propose an optimized stack layer for the optical communication link with minimal modifications to the IEEE 802.3bs standard, tailored for AI training. To validate our approach, we simulate the training of the ChatGPT-2 model on a DC with 32 workers and noisy communication links, showing that the training process tolerate a bit error rate of up to$1 \mathrm{e}-4$.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".