Hybrid Interconnect Infrastructure for Inter-Chiplet Communication in Wafer-Scale Systems
Bibliographic record
Abstract
The semiconductor industry is shifting towards intimate heterogeneous integration of small chips/dies (chiplets/dielets), rather than focusing on large systems-on-chip (SoCs). The chiplet paradigm promotes heterogeneity, scalability, lower non-recurring engineering cost, shorter time-to-market, and simplified testing. That said, important design and manufacturing challenges must be addressed for chiplet-based platforms to take center stage in semiconductor design. Efficient, scalable, and CMOS-compatible interconnect infrastructure, to ensure signal integrity for inter-chiplet communication, is a key design challenge for chiplet-based systems. This challenge is especially important in wafer-scale systems, where efficient package-level long-range communication is critical.A hybrid inter-chiplet interconnect infrastructure for large-scale chiplet-based systems is introduced in this paper. The proposed infrastructure utilizes electrical and silicon photonics-based interconnects for, respectively, short- and long-range inter-chiplet communication. Architecture and characterization of the proposed infrastructure are discussed. Simulation results confirm that the proposed interconnect infrastructure exhibits an energy consumption of 150, 240, and 305 fJ/bit for, respectively, short-, medium-, and long-range inter-chiplet communication on a wafer-scale integration platform. The proposed hybrid communication system significantly outperforms state-of-the-art electrical medium- and long-range communication with an energy requirement in the range of 0.8-1.17 pJ/bit.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".