High-Throughput 0.13-$\mu{\rm m}$ CMOS Lattice Reduction Core Supporting 880 Mb/s Detection
Bibliographic record
Abstract
This paper presents the first silicon-proven implementation of a lattice reduction (LR) algorithm, which achieves maximum likelihood diversity. The implementation is based on a novel hardware-optimized due to the Lenstra, Lenstra, and Lovász (LLL) algorithm, which significantly reduces its complexity by replacing all the computationally intensive LLL operations (multiplication, division, and square root) with low-complexity additions and comparisons. The proposed VLSI design utilizes a pipelined architecture that produces an LR-reduced matrix set every 40 cycles, which is a 60% reduction compared to current state-of-the-art LR field-programmable gate array implementations. The 0.13-μm CMOS LR core presented in this paper achieves a clock rate of 352 MHz, and thus is capable of sustaining a throughput of 880 Mb/s for 64-QAM multiple-input-multiple-output detection with superior performance while dissipating 59.4 mW at 1.32 V supply.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".