FPGA-based Multi-Chip Module for High-Performance Computing
Bibliographic record
Abstract
Current integration, architectural design and manufacturing technologies are not suited for the computing density and power efficiency requested by Exascale computing. New approaches in hardware architecture are thus needed to overcome the technological barriers preventing the transition to the Exascale era. In that scope, we report successful fabrication of first ExaNoDe's MCM prototypes dedicated to Exascale computing applications. Each MCM was composed of 2 Xilinx Zynq Ultrascale+ MPSoC, assembled on advanced 68.5 mm x 55 mm laminate substrates specifically designed and fabricated for the project. Acoustic microscopy, x-ray, cross-section and Thermo-Moire investigations revealed no voids, shorts, delamination, cracks or warpage issues. Two MCMs were mounted on a daughter board by FORTH for testing purposes. The DDR memories on the 4 SODIMMs of the daughter board were successfully tested by running extensive Xilinx memory tests with clock frequencies of 1866 MHz and 2133 MHz. All 4 FPGAs were programmed with the Xilinx integrated bit error ratio test (IBERT) tailored for this board for links testing. All intra-board high-speed links between all FPGAs were stable at 10 Gbps, even under the more demanding 31-bit PRBS (Pseudorandom Binary Sequence) tests.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".